{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Convolutional Neural Network (CNN) Percolation Threshold Demonstration\n",
    "\n",
    "\n",
    "#### John Eric McCarthy II, Senior Petroleum Engineering Undergraduate Student, The University of Texas at Austin\n",
    "\n",
    "##### [LinkedIn](https://www.linkedin.com/in/john-mccarthy2)\n",
    "\n",
    "\n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "\n",
    "\n",
    "#### Honggeun Jo, Graduate Candidate, The University of Texas at Austin\n",
    "\n",
    "##### [LinkedIn](https://www.linkedin.com/in/honggeun-jo/?originalSubdomain=kr) | [GitHub](https://github.com/whghdrms) | [Twitter](https://twitter.com/HonggeunJ)\n",
    "\n",
    "\n",
    "### Workflow for training CNN to classify images\n",
    "\n",
    "This workflow demonstrates design and training of a CNN model to classify for a variety of synthetic labeled images.\n",
    "\n",
    "* a very simple, fast run time, well-documented toy problem to support experiential learning \n",
    "\n",
    "* I demonstrate the impact of scale of the image features, the ability to work with multiple scales  \n",
    "\n",
    "___\n",
    "\n",
    "**Can we train a simple CNN to detect the extent of a randomly located artifact in a set of uniform images**?\n",
    "___\n",
    "\n",
    "We design a simple network with convolution over multiple filters (variable size) to demonstrate this.\n",
    "\n",
    "### Convolutional Neural Networks\n",
    "\n",
    "Extension of the artifical neural network, based on the visual cortex:\n",
    "\n",
    "* extraction of features from overlapping receptive fields, over a hierarchy (not shown) and then recompose the whole image, our perception.\n",
    "\n",
    "* We don’t perceive all the ‘pixels’, our visual cortex interprets and summarizes patterns. Let’s make a machine to do this.\n",
    "\n",
    "**Regularization**: a constraint to reduce the sensitivity of the model to the data, to reduce model variance\n",
    "\n",
    "Receptive Fields\n",
    "\n",
    "* the use of receptive fields is a form of regularization\n",
    "\n",
    "* massive reduction in connections, weights and model parameters\n",
    "\n",
    "* effectively shrinking these potential weights to zero\n",
    "\n",
    "* while integrating / focusing on pixel patterns!\n",
    "\n",
    "We have access to operators to move from layer to layer (feature maps to feature maps) in our convolutional neural networks.  The common operators include:\n",
    "\n",
    "* **Convolution** – a weighting window / filter designed to extract features\n",
    "\n",
    "* **Pooling** – reduction in dimensionality, increase local translation invariance \n",
    "\n",
    "* **Depth-wise Pooling, Down Sampling** – 1x1 filter that combine channels, feature maps\n",
    "\n",
    "* **Activation** – use of an activation function to apply a nonlinear transformation to impart nonlinearity to the system\n",
    "\n",
    "* **Full-connected, feed forward** – see previous lecture \n",
    "\n",
    "For a demonstration of all of these operators, check out this [Python Jupyter Notebook](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/SubsurfaceDataAnalytics_Convolution_Operators.ipynb)\n",
    "\n",
    "#### Objective \n",
    "\n",
    "Michael teaches data analytics, geostatistics and machine learning. To demonstrate the basic construction of a convolutional neural networks, training and prediction.  \n",
    "\n",
    "* Michael uses these examples in his lecture notes, see the lecture posted on his [YouTube channel](https://www.youtube.com/watch?v=za2my_XDoOs&ab_channel=GeostatsGuyLectures).\n",
    "\n",
    "* for any to gain experiential learning with the nuts and bolts of convolutional neural networks\n",
    "\n",
    "Note, we only demonstrate the basics of construction, training and prediction. There is much more about the design and training that we do not cover.\n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries.\n",
    "\n",
    "We will need some standard packages. These should have been installed with Anaconda 3.\n",
    "* [numpy](https://numpy.org/): To generate arrays <br>\n",
    "* [matplotlib](https://matplotlib.org/): Vilsualization purpose <br>\n",
    "* sklearn: for model metrics, confusion matrices, one hot encoder\n",
    "\n",
    "We also will need tensor flow, this will require an install as it is not available in Anaconda\n",
    "* [tensorflow > 2.0.0](https://www.tensorflow.org/learn): Design, compile and train neural network models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                                      # ndarrays for gridded data, our images\n",
    "import matplotlib.pyplot as plt                         # plotting                   \n",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator) # control of axes ticks\n",
    "import pandas as pd\n",
    "from sklearn.metrics import f1_score, accuracy_score, recall_score, confusion_matrix # model metrics\n",
    "import scipy\n",
    "from scipy.ndimage import gaussian_filter               # Gaussian filter for smoothing our images\n",
    "from sklearn.preprocessing import OneHotEncoder         # one hot encoder for our response feature\n",
    "import geostatspy.GSLIB as GSLIB                        # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats                  # GSLIB methods convert to Python \n",
    "import random                                           # pseudo-random values\n",
    "\n",
    "seed = 73073                                            # set the random number seed for repeatability\n",
    "np.random.seed(seed)                                    \n",
    "\n",
    "cmap = plt.cm.inferno                                   # color map for plots\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf                                 # import tensor flow\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.models import Model\n",
    "import visualkeras                                      # visualize CNN architectures\n",
    "\n",
    "assert tf.__version__.startswith('2.')                  # this will give you error if tensorflow < 2.0\n",
    "tf.keras.backend.set_floatx('float32')                  # default float to be 'float32' (Tensorflow only works with float8, 16, 32)\n",
    "\n",
    "physical_devices = tf.config.list_physical_devices('GPU') # constrain memory use to avoid a CUDNN_STATUS_ALLOC_FAILED error\n",
    "#tf.config.experimental.set_memory_growth(physical_devices[0], True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Functions\n",
    "\n",
    "Let's define a couple of convenience functions for workflow brevity and read-ability\n",
    "\n",
    "* train and test accuracy over epochs performance plots\n",
    "* train and test prediction acurracy, confusion matrix plots\n",
    "* visualize the trained CNN filters\n",
    "* visualize the feature maps for a specific image classification case\n",
    "* visualize the last layer, the probability output from the softmax activation function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code below taken from the Simple Convolutional Neural Network Scale Demonstration [Workflow](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/SubsurfaceDataAnalytics_ConvolutionalNeuralNetworks_Classifier_Scale.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 583,
     "status": "ok",
     "timestamp": 1621130828337,
     "user": {
      "displayName": "HONGGEUN JO",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjjMrjKxVvTiBoV4qCmbZQiE5mDJaodEzKxwowf=s64",
      "userId": "13788572619033134064"
     },
     "user_tz": 300
    },
    "id": "di4QxA0b1FoX"
   },
   "outputs": [],
   "source": [
    "def model_performance(history,nepoch):                  # visualize error and loss, training and testing over Epochs     \n",
    "    plt.subplot(1,2,1)\n",
    "    plt.plot(history.history['loss'],c='red')\n",
    "    plt.plot(history.history['val_loss'],c='black')\n",
    "    plt.title('CNN Model Loss')\n",
    "    plt.ylabel('Testing Categorical Entropy'); plt.xlabel('Epoch'); plt.grid()\n",
    "    plt.legend(['train', 'test'], loc='upper right'); plt.xlim([0,nepoch]); plt.ylim([0,1])\n",
    "    \n",
    "    plt.subplot(1,2,2)\n",
    "    plt.plot(history.history['sparse_categorical_accuracy'],c='red')\n",
    "    plt.plot(history.history['val_sparse_categorical_accuracy'],c='black')\n",
    "    plt.title('CNN Model Accuracy')\n",
    "    plt.ylabel('Testing Proportion Correct Classification'); plt.xlabel('Epoch'); plt.grid()\n",
    "    plt.legend(['train', 'test'], loc='upper right'); plt.xlim([0,nepoch]); plt.ylim([0,1])\n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.1, wspace=0.3, hspace=0.3)\n",
    "    plt.show()\n",
    "    \n",
    "def model_cross_validation(model,X_train,X_test,y_train,y_test): # visualize misclassification error for training and testing data \n",
    "    y_train_predict = model.predict(X_train[:,:,:,0].reshape([ntrain,nx,ny,-1])) # predict over the training images\n",
    "    y_train_predict = np.argmax(y_train_predict,axis = 1) # assign the maximum probability category\n",
    "    \n",
    "    y_test_predict = model.predict(X_test[:,:,:,0].reshape([ntest,nx,ny,-1])) # predict over the testing images \n",
    "    y_test_predict = np.argmax(y_test_predict,axis = 1) # assign the maximum probability category\n",
    "    \n",
    "    plt.subplot(121)                                                \n",
    "    train_confusion_matrix = confusion_matrix(y_train,y_train_predict) # calculate and plot train confusion matrix\n",
    "    train_confusion_matrix = train_confusion_matrix/np.sum(train_confusion_matrix) \n",
    "    im = plt.imshow(train_confusion_matrix,cmap=cmap,vmin=0.0,vmax=1.0); plt.xlabel('Prediction'); plt.ylabel('Truth'); plt.title('Training Confusion Matrix')\n",
    "    ax = plt.gca(); ax.set_xticks([0,1]); #ax.set_xticklabels(name)\n",
    "    ax.set_yticks([0,1]);# ax.set_yticklabels(name)\n",
    "    cbar = plt.colorbar(im, orientation=\"vertical\", ticks=np.linspace(0, 1.0, 5))\n",
    "    cbar.set_label('Proportion', rotation=270, labelpad=20)\n",
    "    \n",
    "    plt.subplot(122)\n",
    "    test_confusion_matrix = confusion_matrix(y_test,y_test_predict) # calculate and plot train confusion matrix\n",
    "    test_confusion_matrix = test_confusion_matrix/np.sum(test_confusion_matrix) \n",
    "    im = plt.imshow(test_confusion_matrix,cmap=cmap,vmin = 0,vmax = 1.0)\n",
    "    plt.xlabel('Prediction'); plt.ylabel('Truth'); plt.title('Testing Confusion Matrix')\n",
    "    ax = plt.gca(); ax.set_xticks([0,1]); #ax.set_xticklabels(name)\n",
    "    ax.set_yticks([0,1]); #ax.set_yticklabels(name)\n",
    "    cbar = plt.colorbar(im, orientation=\"vertical\", ticks=np.linspace(0, 1.0, 5))\n",
    "    cbar.set_label('Proportion', rotation=270, labelpad=20)\n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.1, wspace=0.3, hspace=0.3)\n",
    "    plt.show()\n",
    "    \n",
    "    print(f'            Train   Test')                  # print a table with train and test accuracy\n",
    "    print(f'precision: {accuracy_score(y_train.flatten(), y_train_predict):.4f}, {accuracy_score(y_test.flatten(), y_test_predict):.4f}')\n",
    "    print(f'recall:    {recall_score(y_train.flatten(), y_train_predict, average = \"weighted\"):.4f}, {recall_score(y_test.flatten(), y_test_predict, average = \"weighted\"):.4f}')\n",
    "    print(f'f1 score:  {f1_score(y_train.flatten(), y_train_predict, average = \"weighted\"):.4f}, {f1_score(y_test.flatten(), y_test_predict, average = \"weighted\"):.4f}')\n",
    "\n",
    "def visualize_filters(model):                           # visualize CNN filters\n",
    "    for layer in model.layers:\n",
    "        if 'conv' not in layer.name:                    # check if layers is a convolution\n",
    "            continue\n",
    "        filters, biases = layer.get_weights()\n",
    "        fmin, fmax = filters.min(), filters.max()       # normalize the filter weights for visualization\n",
    "        filters = (filters - fmin) / (fmax - fmin)\n",
    "        print('Convolution Layer Name: ' + layer.name + ', Shape: ' + str(filters.shape[0]) + ' x ' + str(filters.shape[1]) + ', Number of Channels: ' + str(filters.shape[3]) + '.')\n",
    "        nch = filters.shape[3]\n",
    "        for ich in range(0,nch):\n",
    "            plt.subplot(1,nch,ich+1)\n",
    "            im = plt.imshow(filters[:,:,0,ich],cmap=cmap,vmin=0.0,vmax=1.0)\n",
    "            ax = plt.gca(); ax.set_xticks([0,1,2]); ax.set_xticklabels([-1,0,1])\n",
    "            ax.set_yticks([0,1,2]); ax.set_yticklabels([-1,0,1])\n",
    "            cbar = plt.colorbar(im, orientation=\"vertical\", ticks=np.linspace(0.0, 1.0, 5))\n",
    "            cbar.set_label('Weights', rotation=270, labelpad=20)\n",
    "            plt.title('Filter ' + str(ich+1))\n",
    "        plt.subplots_adjust(left=0.0, bottom=0.0, right=0.5*nch, top=0.5, wspace=0.4, hspace=0.3)\n",
    "        plt.show()\n",
    "\n",
    "def visualize_convolution_feature_maps(model,image):    # visualize CNN feature maps for a prediction case\n",
    "    ilayer = -1\n",
    "    for layer in model.layers:\n",
    "        ilayer = ilayer + 1\n",
    "        # check for convolutional layer\n",
    "        if 'conv' not in layer.name:\n",
    "            continue\n",
    "        model_trunc = Model(inputs=model.inputs, outputs=model.layers[ilayer].output) # truncate model to output feature maps\n",
    "        feature_maps = model_trunc.predict(X[image,:,:,0].reshape([1,nx,ny,-1]))        \n",
    "        print('Convolution Layer Name: ' + layer.name + ', Shape: ' + str(feature_maps.shape[1]) + ' x ' + str(feature_maps.shape[2]) + ', Number of Channels: ' + str(feature_maps.shape[3]) + '.')\n",
    "    \n",
    "        nch = feature_maps.shape[3]\n",
    "        plt.subplot(1,nch+1,1)\n",
    "        im = fig = plt.imshow(X[image,:,:,0], cmap=cmap)\n",
    "        fig.axes.get_xaxis().set_visible(False); fig.axes.get_yaxis().set_visible(False)\n",
    "        plt.title('Image ' + str(image) + ': ' + names[y[image]])\n",
    "    \n",
    "        for ich in range(0,nch):\n",
    "            plt.subplot(1,nch+1,ich+2)\n",
    "            im = plt.imshow(feature_maps[0, :, :, ich], vmin = -1,vmax = 1.0,cmap=cmap)\n",
    "            ax = plt.gca(); ax.set_xticks([]); ax.set_yticks([])\n",
    "            plt.title('Convolution Feature Map ' + str(ich+1))\n",
    "            cbar = plt.colorbar(im, orientation=\"vertical\", ticks=np.linspace(-1.0, 1.0, 5))\n",
    "            cbar.set_label('Feature Map Values', rotation=270, labelpad=20)\n",
    "        \n",
    "        plt.subplots_adjust(left=0.0, bottom=0.0, right=0.5*nch, top=0.35, wspace=0.4, hspace=0.3)\n",
    "        plt.show()\n",
    "        \n",
    "def visualize_max_pooling_feature_maps(model,image):    # visualize CNN feature maps for a prediction case\n",
    "    ilayer = -1\n",
    "    for layer in model.layers:\n",
    "        ilayer = ilayer + 1\n",
    "        # check for convolutional layer\n",
    "        if 'max_pooling' not in layer.name:\n",
    "            continue\n",
    "        model_trunc = Model(inputs=model.inputs, outputs=model.layers[ilayer].output) # truncate model to output feature maps\n",
    "        feature_maps = model_trunc.predict(X[image,:,:,0].reshape([1,nx,ny,-1]))        \n",
    "        print('Max Pooling Layer Name: ' + layer.name + ', Shape: ' + str(feature_maps.shape[1]) + ' x ' + str(feature_maps.shape[2]) + ', Number of Channels: ' + str(feature_maps.shape[3]) + '.')\n",
    "    \n",
    "        nch = feature_maps.shape[3]\n",
    "        plt.subplot(1,nch+1,1)\n",
    "        fig = plt.imshow(X[image,:,:,0], cmap=cmap)\n",
    "        fig.axes.get_xaxis().set_visible(False); fig.axes.get_yaxis().set_visible(False)\n",
    "        plt.title('Image ' + str(image) + ': ' + names[y[image]])\n",
    "    \n",
    "        for ich in range(0,nch):\n",
    "            plt.subplot(1,nch+1,ich+2)\n",
    "            im = plt.imshow(feature_maps[0, :, :, ich], cmap=cmap)\n",
    "            ax = plt.gca(); ax.set_xticks([]); ax.set_yticks([])\n",
    "            plt.title('Max Pooling Feature Map ' + str(ich+1))\n",
    "        \n",
    "        plt.subplots_adjust(left=0.0, bottom=0.0, right=0.5*nch, top=0.5, wspace=0.4, hspace=0.3)\n",
    "        plt.show()\n",
    "\n",
    "def visualize_last_layers(model, image):                # visualize last layer, category probabilities\n",
    "    ilayer = -1; llayer = -1\n",
    "    for layer in model.layers:                          # get the last dense layer\n",
    "        ilayer = ilayer + 1\n",
    "        # check for convolutional layer\n",
    "        if 'dense' not in layer.name:\n",
    "            continue\n",
    "        else:\n",
    "            llayer = ilayer\n",
    "    if llayer == -1:\n",
    "        print('No dense layer found'); return -1\n",
    "    model_trunc = Model(inputs=model.inputs, outputs=model.layers[ilayer].output) # truncate model to output feature maps\n",
    "    output_layer = model_trunc.predict(X[image,:,:,0].reshape([1,ny,nx,-1]))        \n",
    "    output_layer = output_layer.flatten()*100\n",
    "    \n",
    "    \n",
    "    plt.subplot(1,2,1)\n",
    "    fig = plt.imshow(X[image,:,:,0], cmap=cmap)\n",
    "    fig.axes.get_xaxis().set_visible(False); fig.axes.get_yaxis().set_visible(False)\n",
    "    plt.title('Image ' + str(image) + ': ' + names[y[image]])\n",
    "\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.title('Predicted Category Probabilities')\n",
    "    plt.bar(x = ['Connected', 'Not Connected'], height = output_layer,color='red',edgecolor='black',alpha=0.2)\n",
    "    plt.xlabel('Image Categories'); plt.ylabel('Probability (\\%)')\n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.1, wspace=0.4, hspace=0.3)\n",
    "    plt.show()              \n",
    "    \n",
    "def visualize_testing_predictions(model):               # visualize testing images and predicted probabilities \n",
    "    y_test_predict = model.predict(X_test[:,:,:,0].reshape([ntest,nx,ny,-1])) # predict over the testing images \n",
    "    y_test_predict_cat = np.argmax(y_test_predict,axis = 1) # assign the maximum probability category\n",
    "    \n",
    "    c, r = 4, 5                                         \n",
    "    plt.figure(figsize = (15,15))\n",
    "    for i in range(c*r):\n",
    "        plt.subplot(r,c,i+1)\n",
    "        fig = plt.imshow(X_test[i,:,:,0],cmap=cmap)\n",
    "        fig.axes.get_xaxis().set_visible(False); fig.axes.get_yaxis().set_visible(False)\n",
    "        icat = y_test_predict_cat[i]\n",
    "        plt.title('Image ' + str(i) + ': ' + names[y_test[i]] + ', ' + str(np.round(y_test_predict[i,icat]*100,2)) + \"% \" + names[icat] )\n",
    "\n",
    "            \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=1.3, top=1.6, wspace=0.01, hspace=0.3)\n",
    "    plt.show()\n",
    "    \n",
    "def prediction_boxplot(model):                             # visualize the box plots for predicted probabilities by true category\n",
    "    y_train_predict = model.predict(X_train[:,:,:,0].reshape([ntrain,ny,nx,-1])) # predict over the testing images \n",
    "    y_test_predict = model.predict(X_test[:,:,:,0].reshape([ntest,ny,nx,-1])) # predict over the testing images \n",
    "    c = 'red'\n",
    "    \n",
    "    plt.subplot(121)\n",
    "    box_train = plt.boxplot([y_train_predict[:,0][y_train[:] == 0],y_train_predict[:,1][y_train[:] == 1]],labels=['Connected','Not Connected'])\n",
    "    for i in range(1,4): plt.plot([i+.5,i+.5],[0,1],color='k',alpha=0.3,linestyle='--',linewidth=1)\n",
    "    plt.gca().yaxis.grid(True, which='major',linewidth = 1.0); plt.gca().yaxis.grid(True, which='minor',linewidth = 0.2) # add y grids\n",
    "    plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4)\n",
    "    plt.gca().yaxis.set_minor_locator(AutoMinorLocator())   \n",
    "    plt.ylim([0,1]); plt.xlim([.5,2.5]); plt.ylabel('Prediction Probability of Correct Category'); plt.xlabel('Correct Image Label')\n",
    "    plt.title('Training Image Correct Category Probabilities')\n",
    "\n",
    "    \n",
    "    plt.subplot(122)\n",
    "    box_train = plt.boxplot([y_test_predict[:,0][y_test[:] == 0],y_test_predict[:,1][y_test[:] == 1]],labels=['Connected','Not Connected'])\n",
    "    for i in range(1,4): plt.plot([i+.5,i+.5],[0,1],color='k',alpha=0.3,linestyle='--',linewidth=1)\n",
    "    plt.gca().yaxis.grid(True, which='major',linewidth = 1.0); plt.gca().yaxis.grid(True, which='minor',linewidth = 0.2) # add y grids\n",
    "    plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4)\n",
    "    plt.gca().yaxis.set_minor_locator(AutoMinorLocator())   \n",
    "    plt.ylim([0,1]); plt.xlim([.5,2.5]); plt.ylabel('Prediction Probability of Correct Category'); plt.xlabel('Correct Image Label')\n",
    "    plt.title('Testing Image Correct Category Probabilities')\n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.3, hspace=0.3)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Depth-first search (DFS) algorithmode used to identify images exceeding the percolation threshold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 583,
     "status": "ok",
     "timestamp": 1621130828337,
     "user": {
      "displayName": "HONGGEUN JO",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjjMrjKxVvTiBoV4qCmbZQiE5mDJaodEzKxwowf=s64",
      "userId": "13788572619033134064"
     },
     "user_tz": 300
    },
    "id": "di4QxA0b1FoX"
   },
   "outputs": [],
   "source": [
    "def maxAreaOfIsland(grid):\n",
    "        max_area = 0\n",
    "        for r in range(len(grid)):\n",
    "            for c in range(len(grid[r])):\n",
    "                if grid[r][c] == 1:\n",
    "                    max_area = max(max_area, explore(grid, r, c))\n",
    "        return max_area\n",
    "    \n",
    "def explore(grid, r, c):\n",
    "    if (r < 0 or r >= len(grid)) or (c < 0 or c >= len(grid[r])):\n",
    "        return 0\n",
    "    if grid[r][c] == 0:\n",
    "        return 0\n",
    "    # mark as visited\n",
    "    grid[r][c] = 0\n",
    "    return 1 + explore(grid, r - 1, c) + explore(grid, r, c - 1) \\\n",
    "        + explore(grid, r + 1, c) + explore(grid, r, c + 1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convolutional Neural Network Example\n",
    "\n",
    "Let's try a more complicated problem that will require our model to be location invariant and multiscale. \n",
    "\n",
    "* we will create synthetic images that imitate seismic tomography\n",
    "* we will identify geobodies exceeding the **percolation threshold** using a CNN\n",
    "* the percolation threshold is a mathematical concept in percolation theory that describes the formation of long-range connectivity in random systems\n",
    "* The percolation threshold of a given system is dependent upon the shape of its lattice. We will be generating 2d square lattices that have a threshold of 1/2\n",
    "\n",
    "#### Create Images\n",
    "\n",
    "We make 100 simple images, 80 train and 20 test 12x12 binary, 0 and 1 images.\n",
    "\n",
    "* we do this for a very rapid, fast toy problem\n",
    "\n",
    "We make the random training and testing images and visualize some of the testing models with their labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1080x1080 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(seed)                                    # make synthetic images\n",
    "random.seed(seed)\n",
    "nmodel = 100; ptest = 0.2; nx = 12; ny = 12\n",
    "ntest = int(nmodel*ptest); ntrain = nmodel - ntest\n",
    "X = np.random.rand(nmodel,nx,ny,1)\n",
    "y = np.zeros(nmodel,dtype = int); name = ['anistropic goebody ']\n",
    "X_train = np.zeros([ntrain,nx,ny,1]); y_train = np.zeros(ntrain,dtype = int)\n",
    "X_test = np.zeros([ntest,nx,ny,1]); y_test = np.zeros(ntest,dtype = int)\n",
    "filter_kernel = [[1/144, 1/144, 1/144, 1/144, 1/144, 1/144, 1/144, 1/144]]\n",
    "\n",
    "    \n",
    "for i in range(0,nmodel):\n",
    "    image = X[i,:,:,0]\n",
    "    X[i,:,:,0] = scipy.signal.convolve2d(image, filter_kernel, mode='same', boundary='fill', fillvalue=0)\n",
    "\n",
    "for i in range(0,nmodel):\n",
    "    data = np.concatenate(X[i,:,:,0], axis=None)\n",
    "    mean = sum(data)/ len(data)\n",
    "    target_std = .03\n",
    "    target_value = .15\n",
    "    std = np.std(data)\n",
    "    X[i,:,:,0] = (target_std / std) * (X[i,:,:,0] - mean) + target_value\n",
    "    \n",
    "c, r = 7, 3                                             # visualize labelled synthetic images\n",
    "plt.figure(figsize = (15,15))\n",
    "for i in range(c*r):\n",
    "    plt.subplot(r,c,i+1)\n",
    "    fig = plt.imshow(X[i,:,:,0],cmap=cmap)\n",
    "    fig.axes.get_xaxis().set_visible(False); fig.axes.get_yaxis().set_visible(False)\n",
    "    plt.title(name[y[i]] + str(i))\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.3, top=0.5, wspace=.5, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEWCAYAAABhffzLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAWcklEQVR4nO3dfZRkdX3n8fdHHgRUdkRQARlHjMGwPiAMOllcI6DGR9BsjGENUaMSVzwrxuwqrhqziXk4q5I15mgGQRAEn1BhXXVVRD2aDNoQlkFbRWUQZBbGhxEc0eHhu3/c26ac6Z6p7q5b1T33/TqnTt+6dX/1+3bP1Kdu/ere301VIUnqj3tMugBJ0ngZ/JLUMwa/JPWMwS9JPWPwS1LPGPyS1DMGv9SxJO9K8oZJ1yHNiMfxS+OT5InA+VX1oAmXoh5zj18aQpLdJ12DNCoGv3Z5STYkOT3J15P8OMl7kuzVPvbSJN9O8qMklyQ5aKBdJTk1ybXAtTvaPo0zktyS5CdJrk7yiPaxc5L8ZZJ7AZ8EDkry0/Z2UJKfJbnfQL9HJdmUZI8x/pnUIwa/+uL5wG8DDwV+HXh9kuOAvwZ+DzgQuB54/zbtng08Djh8J9s/BXhC+9wrgOcBPxx8oqraAjwNuKmq7t3ebgI+3z7njD8A3l9Vdyz2l5ZmY/CrL95RVTdU1Y+ANwMn0bwZnF1VV1bVL4DTgd9Msmqg3V9X1Y+q6vadbH8HcB/g4TTfnU1X1cYhazuXJuxJsltb23mL/H2lORn86osbBpavBw5qb9fPrKyqn9LspR88R7s5t6+qzwHvAP4BuDnJ2iT7DlnbxTSfKA4Fngz8pKq+MuwvJs2Xwa++OGRgeSVwU3t78MzKdgz+fsD3B7YdPOxth9tX1dur6ijg39IM+fyXWerY7jC6qvo58EGaTxQn496+Ombwqy9OTfKgJPsBrwM+AFwAvCjJEUnuCfwVcHlVbZjjOebcPsnRSR7XfiG7Bfg5cNcsz3EzcL8k/2ab9e8FXgicAJy/qN9U2gmDX31xAfBp4Lvt7S+r6lLgDcBFwEaaL35/f64n2Mn2+wJnAj+mGQ76IfCWWZ7jG8CFwHeTbJ45KqiqvgzcDVy5gzceaSQ8gUu7vCQbgJdU1WcnXcuOJPkccEFVvXvStWjX5kkp0hKQ5GjgSODESdeiXZ9DPdKEJTkX+CxwWlXdNul6tOtzqEeSesY9fknqmWUxxr///vvXqlWrJl2GJC0rV1xxxQ+q6oBt1y+L4F+1ahVTU1OTLkOSlpUk18+23qEeSeoZg1+Sesbgl6SeMfglqWcMfknqGYNfknrG4JeknjH4JalnDH5J6pllceautDPr161j6+bNY+93zxUreOSaNWPvV1oMg1+7hK2bN3PUAdtNSdK5KzZtGnuf0mI51CNJPWPwS1LPGPyS1DMGvyT1jMEvST1j8EtSzxj8ktQzBr8k9YzBL0k9Y/BLUs8Y/JLUMwa/JPWMwS9JPePsnBqZSU2NDHDd9PREZueUliODXyMzqamRAa6dmppIv9Jy5FCPJPWMwS9JPdNZ8Cc5JMllSaaTfC3JK9v1b0ry/SRXtbend1WDJGl7XY7x3wm8uqquTHIf4Iokn2kfO6Oq3tJh35KkOXQW/FW1EdjYLt+WZBo4uKv+JEnDGcsYf5JVwGOAy9tVr0hydZKzk9x3jjanJJlKMrXJC1pL0sh0HvxJ7g1cBJxWVbcC7wQeChxB84ngrbO1q6q1VbW6qlYf4PHZkjQynQZ/kj1oQv99VfURgKq6uaruqqq7gTOBx3ZZgyTpV3V5VE+As4DpqnrbwPoDBzZ7DnBNVzVIkrbX5VE9xwAnA+uTXNWuex1wUpIjgAI2AH/cYQ2SpG10eVTPl4DM8tAnuupTkrRznrkrST1j8EtSzxj8ktQzBr8k9YzBL0k9Y/BLUs8Y/JLUMwa/JPWMwS9JPePF1qVF+M709MT63nPFCh65Zs3E+tfyZfBLi3D3li0cNaFpw6/wOhVaIId6JKlnDH5J6hmDX5J6xuCXpJ4x+CWpZzyqR1qmJnUoqYeRLn8Gv7RMTepQUg8jXf4c6pGknjH4JalnDH5J6hmDX5J6xuCXpJ4x+CWpZwx+SeoZg1+Sesbgl6SeMfglqWc6C/4khyS5LMl0kq8leWW7fr8kn0lybfvzvl3VIEnaXpd7/HcCr66q3wDWAKcmORx4LXBpVT0MuLS9L0kak86Cv6o2VtWV7fJtwDRwMHAicG672bnAs7uqQZK0vbGM8SdZBTwGuBx4QFVthObNAbj/HG1OSTKVZGqTswFK0sh0HvxJ7g1cBJxWVbcO266q1lbV6qpafcAEpp6VpF1Vp8GfZA+a0H9fVX2kXX1zkgPbxw8EbumyBknSr+ryqJ4AZwHTVfW2gYcuAV7QLr8AuLirGiRJ2+vyClzHACcD65Nc1a57HfA3wAeTvBj4HvDcDmuQJG2js+Cvqi8BmePh47vqV5K0Y565K0k9Y/BLUs8Y/JLUMwa/JPWMwS9JPWPwS1LPGPyS1DMGvyT1jMEvST1j8EtSzwwV/Eke0XUhkqTxGHaP/11JvpLk5UlWdFmQJKlbQwV/VT0eeD5wCDCV5IIkT+60MklSJ4Ye46+qa4HXA68Bfgt4e5JvJPmdroqTJI3esGP8j0pyBs0F048DnlVVv9Eun9FhfZKkERt2Pv53AGcCr6uq22dWVtVNSV7fSWWSpE4MG/xPB26vqrsAktwD2KuqflZV53VWnSRp5IYd4/8ssPfA/X3adZKkZWbY4N+rqn46c6dd3qebkiRJXRo2+LckOXLmTpKjgNt3sL0kaYkadoz/NOBDSW5q7x8IPK+TiiRJnRoq+Kvqq0keDhwGBPhGVd3RaWWSpE4Mu8cPcDSwqm3zmCRU1Xs7qUqS1Jmhgj/JecBDgauAu9rVBRj8krTMDLvHvxo4vKqqy2IkSd0b9qiea4AHdlmIJGk8ht3j3x/4epKvAL+YWVlVJ3RSlSSpM8MG/5u6LEKSND7Dzsf/BWADsEe7/FXgyh21SXJ2kluSXDOw7k1Jvp/kqvb29EXULklagGGnZX4p8GHgH9tVBwMf20mzc4CnzrL+jKo6or19Ysg6JUkjMuyXu6cCxwC3wi8vynL/HTWoqi8CP1pUdZKkkRt2jP8XVbU1CQBJdqc5jn8hXpHkD4Ep4NVV9ePZNkpyCnAKwMqVKxfYVT+tX7eOrZs3j73f66anOeqAA8ber6T5GTb4v5DkdcDe7bV2Xw78rwX0907gL2jeNP4CeCvwR7NtWFVrgbUAq1ev9vyBedi6efNEAvjaqamx9ylp/oYd6nktsAlYD/wx8Ama6+/OS1XdXFV3VdXdNFf0eux8n0OStDjDTtI2E9RnLqazJAdW1cb27nNoTgyTJI3RsHP1XMcsY/pVdegO2lwIPBHYP8mNwJ8BT0xyRPtcG2g+PUiSxmg+c/XM2At4LrDfjhpU1UmzrD5ryP4kSR0Z9gSuHw7cvl9Vfwcc121pkqQuDDvUc+TA3XvQfAK4TycVSZI6NexQz1sHlu+kGZ//vZFXI0nq3LBH9RzbdSGSpPEYdqjnT3b0eFW9bTTlSJK6Np+jeo4GLmnvPwv4InBDF0VJkroznwuxHFlVt0EzvTLwoap6SVeFSZK6MeyUDSuBrQP3twKrRl6NJKlzw+7xnwd8JclHac66fQ7w3s6qkiR1Ztijet6c5JPAv29Xvaiq/qW7siRJXRl2qAdgH+DWqvqfwI1JHtJRTZKkDg176cU/A14DnN6u2gM4v6uiJEndGXaP/znACcAWgKq6CadskKRladjg31pVRTs1c5J7dVeSJKlLwwb/B5P8I7AiyUuBz7LIi7JIkiZjp0f1pLnC+geAhwO3AocBb6yqz3RcmySpAzsN/qqqJB+rqqMAw16Slrlhh3rWJTm600okSWMx7Jm7xwIvS7KB5sie0HwYeFRXhUmSurHD4E+ysqq+BzxtTPVIkjq2sz3+j9HMynl9kouq6j+MoSZJUod2NsafgeVDuyxEkjQeOwv+mmNZkrRM7Wyo59FJbqXZ89+7XYZ//XJ3306rkySN3A6Dv6p2G1chkqTxmM+0zJKkXYDBL0k9Y/BLUs90FvxJzk5yS5JrBtbtl+QzSa5tf963q/4lSbPrco//HOCp26x7LXBpVT0MuLS9L0kao86Cv6q+CPxom9UnAue2y+cCz+6qf0nS7IadpG1UHlBVGwGqamOS+8+1YZJTgFMAVq5cOabyRmv9unVs3bx57P1eNz3NUQccMPZ+JS0P4w7+oVXVWmAtwOrVq5flWcNbN2+eSABfOzU19j4lLR/jPqrn5iQHArQ/bxlz/5LUe+MO/kuAF7TLLwAuHnP/ktR7XR7OeSHwz8BhSW5M8mLgb4AnJ7kWeHJ7X5I0Rp2N8VfVSXM8dHxXfUqSds4zdyWpZwx+SeoZg1+Sesbgl6SeMfglqWcMfknqGYNfknrG4JeknjH4JalnluzsnJKWpu9MT0+s7z1XrOCRa9ZMrP9dhcEvaV7u3rJlYtd7uGLTpon0u6txqEeSesbgl6SeMfglqWcMfknqGYNfknpmlz+qZ/26dWzdvHkifV83PT2xox8kaS67fPBv3bx5YuF77dTURPqVpB1xqEeSesbgl6SeMfglqWcMfknqGYNfknrG4JeknjH4JalnDH5J6hmDX5J6xuCXpJ6ZyJQNSTYAtwF3AXdW1epJ1CFJfTTJuXqOraofTLB/Seolh3okqWcmFfwFfDrJFUlOmW2DJKckmUoytckLLEvSyEwq+I+pqiOBpwGnJnnCthtU1dqqWl1Vqw9wTntJGpmJBH9V3dT+vAX4KPDYSdQhSX009uBPcq8k95lZBp4CXDPuOiSpryZxVM8DgI8mmen/gqr61ATqkKReGnvwV9V3gUePu19JUsPDOSWpZwx+SeoZg1+Sesbgl6SeMfglqWcMfknqGYNfknrG4JeknjH4JalnJnkhFkmal+9MT0+k3z1XrOCRa9ZMpO8uGPySlo27t2zhqAlM037FLnZNEId6JKlnDH5J6hmDX5J6xuCXpJ4x+CWpZzyqR5J2YlKHkUI3h5Ia/JK0E5M6jBS6OZTUoR5J6hmDX5J6xuCXpJ4x+CWpZwx+SeoZg1+Sesbgl6SeMfglqWcMfknqGYNfknpmIsGf5KlJvpnk20leO4kaJKmvxh78SXYD/gF4GnA4cFKSw8ddhyT11ST2+B8LfLuqvltVW4H3AydOoA5J6qVU1Xg7TH4XeGpVvaS9fzLwuKp6xTbbnQKc0t49DPjmEE+/P/CDEZbbBWtcvKVeH1jjqFjj4jy4qrabVnQS0zJnlnXbvftU1Vpg7byeOJmqqtULLWwcrHHxlnp9YI2jYo3dmMRQz43AIQP3HwTcNIE6JKmXJhH8XwUeluQhSfYEfh+4ZAJ1SFIvjX2op6ruTPIK4P8AuwFnV9XXRvT08xoamhBrXLylXh9Y46hYYwfG/uWuJGmyPHNXknrG4JeknlkWwb+zKR7SeHv7+NVJjhx47FVJvpbkmiQXJtlrQjU+PMk/J/lFkj+dT9tJ15jkkCSXJZlu/5avXGo1Djy+W5J/SfLxpVhjkhVJPpzkG+3f8zeXWH1L5fXy/Pa1fHWSf0ry6GHbTrrGcb5eFqyqlvSN5gvg7wCHAnsC/xc4fJttng58kuYcgTXA5e36g4HrgL3b+x8EXjihGu8PHA28GfjT+bRdAjUeCBzZLt8H+NZSq3Hg8T8BLgA+PsH/j3PWCJwLvKRd3hNYsVTqW2Kvl38H3LddftrAa3opvV7mqnEsr5fF3JbDHv8wUzycCLy3GuuAFUkObB/bHdg7ye7APnRzzsBOa6yqW6rqq8Ad82076RqramNVXdku3wZM04TEkqkRIMmDgGcA7+6gtkXXmGRf4AnAWe12W6tq81Kpr7VUXi//VFU/bu+uoznfZ6i2k65xjK+XBVsOwX8wcMPA/RvZ/o846zZV9X3gLcD3gI3AT6rq0xOqsYu28zGSfpKsAh4DXD6asn7FYmv8O+C/AnePsKZtLabGQ4FNwHva4ah3J7nXUqlvCb9eXkzziX4hbRdqMTX+UsevlwVbDsE/zBQPs26T5L4079IPAQ4C7pXkD0Zc35z9j6HtfCy6nyT3Bi4CTquqW0dS1TZdzLJuqBqTPBO4paquGG1J23c1y7ph/467A0cC76yqxwBbgFGPUS/mb7jkXi9JjqUJ1dfMt+0iLabGmfVdv14WbDkE/zBTPMy1zZOA66pqU1XdAXyEZlxuEjV20XY+FtVPkj1o/hO/r6o+MuLaZiymxmOAE5JsoPlYflyS80dbHrD4f+sbq2pm7+/DNG8Eo7SY+pbU6yXJo2iG7U6sqh/Op+2EaxzX62XBlkPwDzPFwyXAH6axhuYj6kaaj6xrkuyTJMDxNONtk6ixi7ZjqbH9250FTFfV2zqobdE1VtXpVfWgqlrVtvtcVXWxt7qYGv8fcEOSw9pVxwNfXyr1sYReL0lW0rzxnFxV35pP20nXOMbXy8JN+tvlYW40R+18i+Zb9v/WrnsZ8LJ2OTQXd/kOsB5YPdD2z4FvANcA5wH3nFCND6TZi7gV2Nwu7ztX26VUI/B4mo+5VwNXtbenL6Uat3mOJ9LRUT0j+Lc+Aphq/5Yfoz0qZAnVt1ReL+8Gfjzw/21qR22XUo3jfL0s9OaUDZLUM8thqEeSNEIGvyT1jMEvST1j8EtSzxj8ktQzBr+0AEn+e5IntcunJdlnnu2T5HNJ9k1yQJIvtTNiPntgm4uTHDRw/y1JjhvZL6He8nBOiWY656q6a4FtN9CcO/KDebR5BvCkqnpVkv8M3E5zxvGnquqYJM+imeHxzwfaPBg4s6qespA6pRnu8WuXkGRVmjnuz23nR//wzF54kuPbSdHWJzk7yT3b9RuSvDHJl4DnJjmp3eaaJH/bbrNbknPadeuTvKpdf06S321D+yDgsjRzsL84yRkDdb00yWxnbz4fuLhdvgPYG7gncHc7M+ZpwP8YbFBV1wP3S/LAkf3h1EsGv3YlhwFrq+pRNGelvjzNhUTOAZ5XVY+kmSjtPw20+XlVPR74IvC3wHE0Z9ce3Q67HEEz0+sj2vbvGeywqt5OM4fLsVV1LM1e+wntXC0AL9q2TesYYGZCuQuA3wY+BbwJeDnNNOM/m6XdlW1bacEMfu1KbqiqL7fL59OcOn8YzcRjM3OpnEszJ/6MD7Q/jwY+X80EZXcC72u3+y5waJK/T/JUmjeUOVXVFuBzwDOTPBzYo6rWz7LpftXM1U5V/aSqnlFVq2mC/ZnARUnObD+5DF6l6xaaTxjSghn82pVs+4VVMfv0uoO2tD9n3a6aC208Gvg8cCrDXeTl3cALmXtvH+DOJLO9/t5Ic2Wsk2g+EfwR8FcDj+9F832AtGAGv3YlKwf2jk8CvkQz4diqJL/Wrj8Z+MIsbS8HfivJ/kl2a9t/Icn+wD2q6iLgDcw+jfJtNJfYA6CaaZcPAf4jcOEctX6T5sIsv5TkYcBBVfUFmqtf3U3z5jV43dtfp5lATVowg1+7kmngBUmuBvajueDJz2n2vD+UZD1NmL5r24bVTON9OnAZzfVVr6yqi2muuvT5JFfRfFdw+iz9rgU+meSygXUfBL5c/3ppvm39b5pZRAe9GXh9u3whzaeGdTRXxZqZ4/3XaGb3lBbMwzm1S0hzibuPV9UjJl0LQJKPA2dU1aVzPH4gzRe4T57Hcz6H5hDPN4yoTPWUe/zSCCVZkeRbwO1zhT788hPGmWkuwD6s3YG3LrZGyT1+SeoZ9/glqWcMfknqGYNfknrG4JeknjH4Jaln/j8U4JUX/MNtqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_data2 = np.concatenate(X[1,:,:,0], axis=None)\n",
    "GSLIB.hist_st(hist_data2,np.min(hist_data2),np.max(hist_data2),log=False,cumul = False,bins=10,weights = None,xlabel='porosity (%)',title='porosity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of Porous and Non-Porous Cells in Samples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x1080 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,15))\n",
    "for i in range(c*r):\n",
    "    plt.subplot(r,c,i+1)\n",
    "    non_porous = [x for x in np.concatenate(X[i,:,:,0], axis=None) if x <= .15]\n",
    "    porous = [x for x in np.concatenate(X[i,:,:,0], axis=None) if x >= .15]\n",
    "    plt.hist(non_porous, bins=5,label='non_porous',alpha=.5)\n",
    "    plt.hist(porous, bins=5,label='porous',alpha=.5)\n",
    "    plt.title(name[y[i]] + str(i))\n",
    "    plt.legend()\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.3, top=0.5, wspace=.5, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of Porous and Non-Porous Cells in All Samples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots = []\n",
    "for i in range(0,nmodel):\n",
    "    plots.append(X[i,:,:,0])\n",
    "non_porous = [x for x in np.concatenate(plots, axis=None) if x <= .15]\n",
    "porous = [x for x in np.concatenate(plots, axis=None) if x >= .15]\n",
    "plt.hist(non_porous, bins=5,label='Non Porous',alpha=.5)\n",
    "plt.hist(porous, bins=5,label='Porous',alpha=.5)\n",
    "plt.title(\"Number of Porous and Non-Porous Cells in All Samples\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Proportion of Sand in All Samples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = np.zeros(nmodel,dtype = int)\n",
    "for i in range(0,nmodel):\n",
    "    occurrences_more_than_15 = X[i,:,:,0] > .15\n",
    "    z[i] = occurrences_more_than_15.sum() \n",
    "z = z /144 * 100\n",
    "plt.hist(z[:], bins=5,alpha=1)\n",
    "plt.title(\"Proportion of Sand in All Samples\")\n",
    "plt.xlabel('Proportion of Sand in Samples (%)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create image labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64 images are not connected\n",
      "36 images are connected\n"
     ]
    }
   ],
   "source": [
    "names = ['connected', 'not connected']\n",
    "for i in range(0,nmodel):\n",
    "    array = X[i,:,:,0]\n",
    "    new_array = np.where(array < .15, 0, 1)\n",
    "    new_array = new_array.tolist()\n",
    "    if maxAreaOfIsland(new_array) > 72:\n",
    "        y[i] = 0\n",
    "    else:\n",
    "        y[i] = 1\n",
    "not_connected_occurrences = np.count_nonzero(y == 1)\n",
    "print(str(not_connected_occurrences) + ' images are not connected')\n",
    "connected_occurrences = np.count_nonzero(y == 0)\n",
    "print(str(connected_occurrences) + ' images are connected')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x1080 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c, r = 7, 3                                             # visualize labelled synthetic images\n",
    "plt.figure(figsize = (15,15))\n",
    "for i in range(c*r):\n",
    "    plt.subplot(r,c,i+1)\n",
    "    fig = plt.imshow(X[i,:,:,0],cmap=cmap)\n",
    "    fig.axes.get_xaxis().set_visible(False); fig.axes.get_yaxis().set_visible(False)\n",
    "    plt.title('Image '+ str(i) + str(': ') + names[y[i]])\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.3, top=0.5, wspace=.5, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create binary images to increase speed of CNN model\n",
    "\n",
    "* the yellow is representative of porous media exceeding 15% porosity \n",
    "* the black represents porous media below 15% porostiy\n",
    "* this is done so that the ideal porous media for connectivity is highlighted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c, r = 7, 3                                             # visualize labelled synthetic images\n",
    "plt.figure(figsize = (15,15))\n",
    "for i in range(0,nmodel):\n",
    "    array = X[i,:,:,0]\n",
    "    X[i,:,:,0] = np.where(array < .15, 0, 1)\n",
    "    for i in range(c*r):\n",
    "        plt.subplot(r,c,i+1)\n",
    "        fig = plt.imshow(X[i,:,:,0],cmap=cmap)\n",
    "        fig.axes.get_xaxis().set_visible(False); fig.axes.get_yaxis().set_visible(False)\n",
    "        plt.title('Image '+ str(i) + str(': ') + names[y[i]])\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.3, top=0.5, wspace=.5, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,nmodel):\n",
    "    if i < ntest:\n",
    "        X_test[i,:,:,0] = X[i,:,:,0]; \n",
    "        y_test[i] = y[i]\n",
    "    else:\n",
    "        X_train[i-ntest,:,:,0] = X[i,:,:,0]; \n",
    "        y_train[i-ntest] = y[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1. Define Classification Convolutional Neural Network \n",
    "\n",
    "Now we will define our convolutional neural network\n",
    "\n",
    "* with keras frontend for tensor flow it is not too dificult to design our network\n",
    "\n",
    "* the overall architure looks like this:\n",
    "\n",
    "![image info](https://miro.medium.com/max/2000/1*vkQ0hXDaQv57sALXAJquxA.jpeg)\n",
    "\n",
    "Image taken from [blog post](https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53)) by Sumit Saha.\n",
    "\n",
    "We cycle multiple layers of:\n",
    "\n",
    "* convolution and activation with ReLU\n",
    "\n",
    "* max pooling\n",
    "\n",
    "After multiple cycles we have learning the features in the images we then finish with:\n",
    "\n",
    "* flattening the feature into a 1D vector\n",
    "\n",
    "* feed-forward, fully-connected artificial neural network with 2 output nodes for the probability of each category\n",
    "\n",
    "As specified below the model includes:\n",
    "\n",
    "* cycles of convolution with 3x3 kernels, stride = 1 for feature maps extent reduction (one half)\n",
    "\n",
    "* feature maps transition from 12x12x1 [nx,ny,nchannel] over multiple steps to reduce featured map extent while increasing the depth and then flattens to a vector fully connected with 2 output nodes\n",
    "\n",
    "#### We begin by tuning the hyperparameters of multiple models at once to see which model performs the best:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " Result when 4 initial channels are used\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " Result when 5 initial channels are used\n"
     ]
    },
    {
     "data": {
      "image/png": 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5xGVo9DfffJPptiZtu3fvZuvWrVkKtDp16tCyZUsA2rRpw86dOy8gaKVUkTN//vnBfTZsgIsvdt0eHw8REdbUJblp/tq1K5QrZzWrDQ62mtC2b29Nl2Kv8VSqIBSCMrqolc/ah1MppZRbZcqUcbxfsGAB8+bNY+nSpfz999+0atXK7dDpISEhjveBgYGkpaUVSKxKqULCnhACLFqUdfuiRRAbm/u+lqVKQZ8+8P33Vm1ns2bWIELh4Rces1JFTFErn7WGUymlCptc1kTml7Jly3LSQ7+qpKQkKlasSOnSpdmyZQt//fVXAUenlCr0MjKsGserr4alS63azLvuOr99/35rUKA77sjb+fv3h6lToXFj+O03KASDoagSyA9ldFEvnzXhVEopBUBERASdOnWiefPmhIWFUaVKFce2nj178v7773PxxRfTqFEjLr30Uj9GqpQqlP76y0oq+/eHoCAr4RQ5X5sZH2+9xsXl7fxXXw2vvmo1p42Kyp+YlSoCinr5rAmnUkoph6+//trt+pCQEGbPnu12m70fSGRkJBs2bHCsf/DBB/M9PqVUIWZvTtunDxw/DtOmwc6dYO9rFh8PpUtD69Z5O3+pUjB2bH5Fq1SRUpTLZ+3DqZRSSimlLoyIlXB27Qrly1v9NMG1H+eiRXDppTqirFIljCacSimllFLqwmzYANu3W81pAZo3t/pY2pvRnjgBf/99PhFVSpUYmnAqpZRSSqkL83//Z/XVvPpq63NAAHTqdD7hXLLEGlQor/03lVJFliacSimllFLqwkyfDh06QNWq59fFxcGWLXDokJV4BgZaTWqVUiWKJpxKKaWUUirvdu6ENWvON6e1szefXbzY6r/ZurXOm6lUCaQJp1JKKaWUyjv7vITXXOO6vm1bCAmB33+HZcu0/6ZSJZQmnEoppQA4fvw47777bp6OfeONNzhz5kw+R6SUKvRefBEmTYI774T69V23hYRA+/YwZQokJ2v/TaXyqKiXz5pwKqWUAop+gaaUKmBvvw2PPQY33GC9dycuDk6dst5rDadSeVLUy+cgv15dKaVUofHoo4+yfft2WrZsSbdu3ahcuTLffvstycnJ9O/fnwkTJnD69Gmuv/569uzZQ3p6Ov/73/84cOAAe/fupUuXLkRGRjJ//nx/34pSytemTIF777VGpZ0yxRoQyB17ktmoEURFFVR0ShUrRb181oRTKaUUAC+++CIbNmxg7dq1/Pbbb3z//fcsX74cEeGqq67izz//5NChQ1SvXp1ffvkFgKSkJMqXL89rr73G/PnziYyM9PNdKKV87vvvYcQI6NYNvvkGgoM979uxo5WMXnZZwcWnVDFT1MtnTTiVUqqQGTNmDGvXrs3Xc7Zs2ZI37AN7eOG3337jt99+o1WrVgCcOnWKrVu3EhcXx4MPPsgjjzxC3759idM+WUqVLLNmwZAh1hQo06db/TSzU64czJwJzZsXTHxK+Zi/y+iiWD5rwqmUUioLEeGxxx7jjjvuyLJt1apVzJo1i8cee4zu3bvz5JNP+iFCpVSBW7AABgyAiy+GX36BMmW8O65nT5+GpVRJUhTLZ004lVKqkMlNTWR+Klu2LCdPngSgR48e/O9//2Po0KGEh4eTmJhIcHAwaWlpVKpUiWHDhhEeHs6UKVNcjtUmtUoVU8uWQb9+ULcu/PorlC/v74iU8gt/lNFFvXzWhFMppRQAERERdOrUiebNm9OrVy9uuOEGOnToAEB4eDhffvkl27Zt46GHHiIgIIDg4GDee+89AG6//XZ69epFtWrVdNAgpYqbdeusWsoqVWDuXNAvlpQqUEW9fNaEUymllMPXX3/t8nn06NEun+vVq0ePHj2yHHfvvfdy7733+jQ2pZQf/POPNThQeDjMmwfVq/s7IqVKpKJcPus8nEoppZRSKqudO+HKK6338+ZB7dr+jEYpVURpDadSSimllHK1b5+VbJ46ZQ0W1KiRvyNSShVRmnAqpZRSSilXQ4bA/v3w++/QooW/o1FKFWGacCqlVCEhIhhj/B2Gz4mIv0NQSmUnPh4WLoQ334T27f0djVKFQkkoo31VPmsfTqWUKgRCQ0M5cuRIsU/GRIQjR44QGhrq71CUUp48/zxUrgy33ebvSJQqFEpCGe3L8llrOJVSqhCoWbMme/bs4dChQ/4OxedCQ0OpWbOmv8NQqmg4cwYCAyEkJOd909Lgv//A+Y/i2rWhVCn3+584AeXKua5bswbmzLGSzrCwPIetVHFSUspoX5XPmnAqpVQhEBwcTJ06dfwdhlKqsOnVC44fh/nzoVKl7Pd9+GF4/XXXdU2bWoP+REW5rn/5ZXjkEXjjDbjvvvPrX3jBSkLvuisfgleqeNAy+sJok1qllFJKqcIoNRX++gvWrbMSz5MnPe976BC8/z706QNffWUtb78NO3ZAjx5W0mr33ntWchoVBaNHwyefWOv//Re+/x7uvhvKl/fprSmlSg6t4VRKKaWUKow2b4aUFBg2DKZOhX79YNYsKF06676TJsG5c/DKK9C48fn1devC1Vdbiehvv8GPP1q1l337WuccMABGjoTwcPj1V6vp7pgxBXaLSqniTxNOpZRSSqnCaO1a63XcOOjdG4YOhYED4f/+z7Vf5okTVm3mtde6Jptg1YxOnQrXXw+xsVZtadeu8N13EBoK06dbNaBDh1r733GHNWCQUkrlE21Sq5RSSilVGK1ZYw3c07ChNS/mBx/A7Nlwww3WAEF2770HSUnw2GPuzzNggNVsdu1aa5qTn36ykk2waktnzrTm2jQGHnzQ57ellCpZtIZTKaWUUqowWrMGLr7YGqUWrKavp0/D/ffDiBHw6aeQnGwNFNS9O7Rp4/lcN98MrVtDvXpZm+SWL2/Nu5mYaI1qq5RS+UgTTqWUUkqpwkbEqpEcMsR1/ZgxVhPap56CsmWtUWgPHLCa3ebkoos8bytTxqpJVUqpfKYJp1JKKaVUYbNzp9VMtmXLrNv+9z9rxNpXXrEG+enQAS67rKAjVD5w5MgRQkJCCA8P9+l1zp07R1JSElWqVMn3c+/bt4+IiAhKeZr/1UlSUhIiQoUKFfI9DlV4aB9OpZRSSqnCxj5gUKtWWbcZAy+9BHfeaTWpffxxa50q8rp06cLo0aN9fp2JEyfSrFkzUlNT8/W8qampNGnShLfeesur/YcPH87gwYPzNQZV+GjCqZRSSilV2KxZAwEB0Ly5++3GwDvvwNat1pQnqsjbv38/69evZ+PGjT6/1saNGzly5Ahr1qzJ1/MmJiaSlJTk9T1s2LChQO5X+ZcmnEoppZRShc3atdYUJ+7m3LQLCID69QssJOVb8fHxAOzatcvn17Jfw37N/D6vN/eQkZHB7t272bt3b77XtKrCRRNOpZRSSqkLkZ4Omzbl7znXrHHfnFYVW4sWLQKsPpDJyck+vVZCQoLLNfP7vPbX7Bw8eJDk5GQyMjJITEzM1zhU4aIJp1JKKaVUXmVkWFOONGsG//2XP+c8fBj27HE/YJAqtpxrG/fs2eOz6yQnJ7N//37ASjhFJN/Oba/Z3L17NxkZGV7tm/m9Kn404VRKKaWUygsRuOsu+Oor63N+1dJkN2CQKpZOnDjB33//zWW20Ya9qSHMK3sye9lll3H48GG2bNmSb+e2x52cnMzBgwe92jfze1X8aMKplFJKKZVbIvDww/DBB9Cvn7XuyJH8Obd9IBet4SwxlixZQkZGBkOHDgV8W+NnT+7s18rPfpy5qbXUGs6SQxNOpZRSSqncevZZax7Me+6BN9+01h09mj/nXrsWatWCiIj8OZ8q9BYtWkRgYCADBw7EGOPTBMx+7q5du1K5cuV87ce5a9cu6tsGsvIm4SxXrhxRUVGacBZzmnAqpZRSSuXGwYPw5JMwZAhMmnQ+MczPGk6t3SxR4uPjadWqFZUqVaJq1ao+bWJqP3etWrWIi4vLtxpOESEhIYG4uDiX62QXR3R0NNHR0dqktpjThFMppZRSKjcOHLBe+/e3piYJD4egoPxJOM+cgX/+0f6bJUhycjLLli1zJGoxMTE+r+GsVq0aISEhxMXFsXPnznwZpOjo0aOcOXOGiy++mLJly3pVwxkTE+Pz+1X+pwmnUkoppVRuHDtmvVasaL0aY9Vy5keT2vXrrZFvtYazxFi5ciXJycmOhNPXNX67du0iOjoagNjYWCB/pkexJ40xMTFER0d7lXDaazh37dqVr6PlqsJFE06llFJKqdywJ5aVKp1fFxGRtxrO1FSYPh2+/tpaPv3UWq81nCWGPdmzJ3++TsASEhKIiYkBoEWLFoSHh+dLs1p7kuxNM9nTp09z5MgRx76nT5/maH71gVaFTpC/A1BKKaWUKlIy13CClXzmJeH85Re49lrXdTVqgC0hUMVffHw8jRo1IioqCrBqCJOTkzl06BCVK1fO12uJCLt27eLqq68GICgoiI4dO+ZLwulcwxkTE8Py5cu92jckJMSxLkIHyiqWfFrDaYzpaYz5xxizzRjzqJvt5Y0xM4wxfxtjNhpjbvFlPEoppZTS8vmCuUs489qkdts263XVKqvv5j//wIYNVjNdVexlZGSwePFiR3NawNHc1RfNag8dOkRycrLjGmDVrG7YsIFj9p/rPEpISCAsLIyIiAiio6M5cuQIp0+f9rgvnK8NdV6nih+fJZzGmEDgHaAX0BQYYoxpmmm3u4FNItIC6Ay8aowp5auYlFJKqeLCGBNljBlnjJlsjPnEvnhxnJbPF+roUWuwoHLlzq/La5PaXbus87RuDQ0bWkuFCvkWqircNm7cyPHjx90mnL4YSMee1MU41aDHxcUhIixZsuSCzm3vk2mMyfEe7OudE04dOKj48mWT2kuAbSKyA8AYMw24GtjktI8AZY0xBggHjgJpPoxJKaWUKi5+AuKBeUB6Lo7T8vlCHTtmJYUBTt/b57VJbUJCnpvPZmRkMG/ePLp164bJZY1oWloa3333nUsNVL169ejSpYvb/ZctW0bDhg2p6Fyrmwciwty5c7nyyisJCMi53mPVqlUcP37cq3Pv2LGDP/74w/HZGEOvXr2oXr16ln3PnTvHX3/9RefOnd2ea9myZaxfv97jtSpXrsxVV13lVVwzZ85k//79brctXboUON9/E84ng5lr/NLS0vjjjz88/nuvXbuWX375hW22WvOgoCD69+9P+fLlHfs4J3p2l1xyCcHBwcTHx9OnT58s583IyGDu3Ll07949258z+6izzvewa9cumjRp4nbfwMBAqlevTkBAAKGhoZpwXqD58+fTvn17Spcu7e9QsvBlwlkD2O30eQ/QPtM+bwM/A3uBssAgEcnIfCJjzO3A7QBRUVEsWLDAF/EWO6dOndJn5SV9Vrmjz8t7+qyUD5UWkUfycFy+lc9QMsvoJlu2UC40lGVO9xqdlETdc+f489dfybD1ScuO/XdDm02bSImMZH0entv8+fN5+umnef/992nUqFGujl2xYgUPP/ywy7qgoCBmzZpFcHCwy/qUlBT69u3L4MGDufXWW3Mdp7NNmzZx99138+STT3pMbp2ve9VVV9GjRw8qeFHr+9BDD7Fy5UqXdd26dWPcuHFZ9v3222957733+Pbbbx19J50NHDiQIzl8gfDVV1+5TWadHThwgMGDB2e7T61atUhISHAkXCJCWFgYS5YsoXXr1o795syZw8SJE3n77bdp1qxZlvMMHTqUvXv3uqxbuHAhN998s+OzPSHfvXu3SyLfoEEDZs6cSc+ePbOcNz4+nieffJKXXnqJdu3aebyPrVu30rFjRxYsWMAB29RBv/32m6OPprPly5cTGRnp6DsaFRXFypUrC/T3R3EqnxMTExk2bBh33HFHjj9vfiEiPlmA64CPnD7fCLyVaZ+BwOuAAeoD/wHlsjtvw4YNRXln/vz5/g6hyNBnlTv6vLynz8p7wErxUZlUHBfgWaB3Ho7zSfksJamM7tFDpF0713WTJ4uAyO7dXp3C8buhUiWRUaPyFMaoUaMEkJ9//jnXx7733nsCyMqVK2X37t3y2muvCSDbt2/Psu+2bdsEkBtuuCFPcTr78ssvBZC77747x33j4+MFkA4dOuS4b2pqqpQpU0ZuvfVW2b17t+zevVuuuuoqiYmJcbt/3759BZA///wzy7YzZ84III888ojjXM7L999/L4DMmzcvx7jmz58vgHzzzTduz7V79245c+ZMluOaNm0q/fv3d1l3yy23CCAvvPBClv337NkjgNxyyy2O81588cXStWtXl/1Gjx4t4eHhkpGR4bL+oYcekuDgYLex3HfffQLIuHHjPN7n2bNnBZCnn35aRKx/j8DAQHn88cfd7n/ZZZdJXFyc4/OVV14pl1xyicfz+0JxKp8//vhjAaRPnz4+Of+Fls++HDRoD1DL6XNNrG9Knd0C/Gi7l21YBVpjH8aklFJKFRejgZnGmHPGmJO25YQXx2n5fKGOHXMdMAjOT5GSm2a1p05Z/UHz2KTWPp3G4cOHc33srl27CA4OplWrVtSsWZMWLVo41rvb19O2vFwX8GpUVPs+9tqy7Kxdu5bTp0/TvXt3atasSc2aNbniiitISEhg9+7dLvvaB+pxjseZff/mzZs7zuW82GsdvXke9n1at27t9lw1a9YkLCwsy3ExMTFZmtTan4e7OTPt69q3b+847+WXX85ff/1FamqqSzz2fpbO4uLiSE1NZcWKFR7Pnd1cnXv27HHEDVZteY0aNbLtw+ncjzQmJkab1F4A+8/G4sWLychw2xjFr3yZcK4AGhhj6tgGGhiM1TzH2S7gCgBjTBWgEbDDhzEppZRSxYKIlBWRABEJtb0vKyLlcj5Sy+cLdvRo1oTTPp2Du5Fq16yB7duzrrf/ge3Un85bx44dY8OGDUDeEs6EhARq1qzp6EeZ3Uih9nX5MYqo/Rzr16/PsW9mbhJO+77OfSHt7zMnt5s2bXKMyJrd/UZ7+HepUaMGxhivnod9n5o1a+a4rzP7XJx2+/fvZ9u2bYSGhrpNKuLj4wkPD6d+/fqOdbGxsZw5c4Y1a9Y41mVO9Ow6duzoOI+zEydOsHbtWkJDQ1m2bBnJycnZ3qfzM/M0F2d6ejp79uzJsu/+/fs5d+6c2/Or7MXHxxMaGsrx48fZuHGjv8PJwmcJp4ikAfcAvwKbgW9FZKMx5k5jzJ223Z4BOhpj1gO/A4+ISO5/ayqllFIlkDHmKmPMK7alrzfHaPmcD44dO1+jaWdPON3VcA4dCmPGZF1vTyjyUMO5ePFie/NnDh06lOvj7TVddvaEKLsazsTERNLSLmzsqF27dhEUFIRI9qOipqens2TJEoKCgjh9+jRJSUnZnnfRokXUqVOHGjVqONa1aNGCsmXLZqmZsydVQUFB2d6vp4SzVKlSVKtWzesazipVqhAaGprjvs5iYmI4fPiwY1An+z2MGDGC48ePO75ssIuPj6dDhw4EBgY61tlHvnVOIhMSEtzeV0REBM2aNcuScC5dupSMjAxGjBhBcnJylj6yzvcJWRNOd89o3759pKWlZdkXzteUKu/t27eP7du3M2LECMC71gMFzafzcIrILBFpKCL1ROQ527r3ReR92/u9ItJdRC4SkeYi8qUv41FKKaWKC2PMi1jNajfZltG2dTnS8vkCZGTkrkmtCOzcCe5qHey1P3mo4YyPjyc4OJjIyMg8N6l1rukKDQ2latWq2SZgGRkZWQalyct1r7jiCseoqJ5s2LCBpKQkevXqBZClWawzESE+Pt5lahGAwMBAOnbsmOU6ixYtolq1alx88cUe7zcgIMAlec3M2yagnmoUc2JPwOz3HR8fT+nSpbnvvvsc92B3/Phx1q9fn+X+q1WrRr169Rz3f+bMGQ4fPuwxkY6Li2PJkiWkp58f9HrRokUEBgby4IMPOuJwJyEhAWOMS01uTEwMe/bscTmffV/7dud9nbcp79l/Fm666SaqV69e8hJOpZRSSvlMb6CbiHwiIp8APW3rlC+dPGklnZ6a1GZOOI8fh7NnraTz7FnXbbt2QVAQVKuW6zAWLVpE27ZtqVmzZq4TzrS0NBITE7MkHp6aQNqTCfv7vBIREhISaNy4MW3atMn2D2P7tqFDh+Z43X///ZdDhw65NKe1i42NZcOGDRx1aupsT07d9ZO0X6t69epZRut15ulZuTuXpwQvO5nnpoyPj+fSSy+lQYMG1KhRw+XZLVmyBBHxeP+LFi1CRBzJq6cEOC4ujpMnT7Ju3TrHuvj4eFq1akXt2rVp3Lixx36cu3btolq1apQqdX663ujoaNLS0ti3b1+WfZ3v0d39Ku/Zv4xo1aoVcXFxxMfHO1o/FBaacCqllFJFVwWn9+U97aTyka3vX5YmtaGhULp01j6ciYnWqwj8+6/rtoQEqFkTnJpBeuPs2bOsWLGC2NjYPNVw7t27l/T0dLcJp6cav4suusjxPq+OHz/OqVOniI6OJjY2lhUrVnjssxcfH0/NmjUdSVR217UnQZlr+JzX2Zvv2gcRio2Nddxv5j/OMzc3dic6Oprdu3dnO0CLiHh1Lneca/xOnDjB33//TWxsLMYYYmNjXZKK+Ph4goKCaN8+8+xG1v0fOXKELVu25Ng3NXOf1+TkZJYtW+Z4hrGxsR4HpXFXk+s8F2fmfTPHUbNmTYwxmnDmgf3LiODgYGJjY0lMTCx0NcWacCqllFJF0wvAGmPMFGPMZ8Aq4Hk/x1T82RPOzDWcYCWhmWs47QknwJYtrtt27cpTc9rly5eTmppKXFwcUVFRue7Daf+j3l2CkDkBsydNnTp1Ai6shtM50YiLiyMlJcXtqKgiwqJFi4iLi6NatWoe+1raxcfHExUV5XYu0ksuucSl+a79NS4ujujoaE6dOpVl8CJvmsHGxMSQnJzMwYMHPe5z+PBhzp49m6cmtdWrVycgIIBdu3Y5+lHaE7+4uDgSExPZuXOn457atGlD6dKls5zHuR+np393u+joaKKjox3PaNWqVZw7d87luu76j4L7mlxPA1ElJCRQqVIlwsPDHetCQkKoWrVqoUuUCrukpCTWrVvn8m8Eha8fpyacSimlVBEkIlOBS4EfbUsHEZnm36hKAHsNZuYaTrCa1WaXcG7e7LotjwmnvUavU6dOearh9FTTFR0dzdmzZ13Od+jQIc6dO0fjxo2JjIy8oBoo57579gTW3R/G//33H3v37iUuLo6AgAAqV66cbSISHx/vqP3LLCwsjHbt2rlMKVKuXDkuuugit/0GMzIy2L17t1c1nJB9zWtOgw9lx3lakfj4eAIDA7n00kuB80nFokWLOHfuHCtWrHBbuwvQoEEDKleuzKJFi0hISCAgIIDq1at7vG5cXJyjCa7zz5nzdTP/m3l6Zp6ekadaX0817MqzzF9GNG/enPLly2vCqZRSSqm8M8Y0tr22Bqphzau5G6huW6d8KbsazogIz01qa9RwqeE06emwZ0+eRqiNj4+nWbNmVKpUicjISI4fP+4y12JO7H/U16pVy2W9uwTBuVbsQudKdE7AIiIiaNq0qds/jDNPcVK5cmWP1927dy87duxw23/RLjY2lpUrV3L27Fni4+Pp2LEjgYGBbu/3wIEDpKSk+D3hhPNzcS5atIhWrVo5agSbNWvmSCpWrFhBSkqKx4TTuQnurl27qFGjBkFBQR6vGRsby/79+9m+fTvx8fE0atSIypUrA1C7dm2qV6+epR/noUOHSE5OzlJzWrZsWSpWrOg24XRXy6pzceae/csIe3Nq+0BZ2c2Z6g+acCqllFJFy1jb66tullf8FVSJkVPC6a6GMzISWrRwqeEsdeQIpKfnuobTPl2IPcGIjIwEcBkUJye7du0iMjKSMmXKuKx31+fOOWnydqCc7K4bEhJCVFQU4H5UVLBq7ipWrEizZs2A7BPO7Ppv2sXFxZGamsrs2bPZtGmTY9+cEuzseDOqqrvRWHMjOjqabdu2ufSjBCup6NSpE/Hx8Y7k3F4L6U5cXBw7d+5kyZIlOcZiv87ChQtZvHixy3WNMW4Hpcmub6i7nxlPAynZaziz6xerXC1atIjWrVu7NE+Oi4tj8+bNeRq92lc04VRKKaWKEBG53fa2l4h0cV7QUWp9L7smtZ76cNaoAU2aWIMG2ZKr0AMHrO25TEb+/vtvTp486UgE7MlbbvpxZvcHv3278772bZ4G2cntdQMCrD8/4+LiOHHiBOvXr3fZLz4+nk6dOjn2q1KlComJiW5rcePj4ylTpgytWrXyeN1OnTphjOHFF190XBesRDYkJMTj/WanfPnylC1bNscaztKlS1PJ3c+KF6Kjo0lMTHTpR2kXFxfHli1bmD59Ok2bNiXCPkqyG/Zjt23bluN9NWnShEqVKvHBBx9w7NixLDXHmfuPQvY1uZmbySYlJXHixAmP+yYnJ+dpXtmSKPOgTnbOTa4LC004lVJKqaJpiZfrVH46dgyCg60RaTOzN6l1TsjsCWfjxnDunGPuzRB7wpnLGk77H5H2RMBew5mb2gxPfegqVapE6dKls9T4lSlThkqVKhETE+N2kJ28Xtddn8CDBw/yzz//uPwRXaVKFY9zgMbHx9OhQ4dsm4lWrFiR5s2bs2LFCkqVKkW7du0Aq8Yuc0LkbQ2nMSbHJqD2+3XXt9QbzjG4S/wAVq5cmW3tLkCLFi0cNWA53VdAQIBjBGHn62S+rnMyk90zy/yMctrXeR+VvZUrV5KcnJzl36hdu3aEhIRowqmUUkqpvDHGVDXGtAHCjDGtjDGtbUtnwE0WpPLVsWNWc1p3SUREhFWDeeLE+XXONZzg6McZah/dNJcJZ3x8vKO2EXKfcNrnwnT3B789icpc42dPmjyNOuqtzAlndHQ0tWrVcvnDePHixYBrgmXvQ5g5EbGP0Jld/007+z7t2rUjNDTUJYbM91u+fHnKlSuX4zlzamLs6Tl7y/6sGjVq5KjJtmvbti0hISFA1mQ0s6CgIDp06OByzuzYz1etWjXq1Knjss25/6hdQkICZcuWpXz5rDMzRUdHk5SURFJSkmNfT3Fc6M9XSZN5UCe7kJAQl4GyCgPPXwcppZRSqjDqAQwHagKvOa0/CYzzR0AlytGj7pvTwvn1R45A+fKQkgIHD56v4QQr4ezd26rhjIiATP0osyMixMfHc+WVVzrWZZdwzp8/n1KlSrn8Qeo8F6Y77mr87EmTcw1Uy5YtPca5aNEiUlJS6Nq1q2NdSkoK+/bty5KAxcXFMWfOHB544AEA/vrrL0JDQ2nbtq1jnypVqgBWIuJcm7NkyRJEJMcaPvt13nvvvSz7xsTEMHv2bJf79XaQn+joaJYtW+Zx+65du7Jt6uvN+cF9/9SQkBAuueQS4uPjvb7/uXPnenVvzlNsZK6dtfcfnTlzpuPf7Ndff/VYk2u/3v3330/FihUdU6pkl3C+9957LF26NMc4L9Tu3buZMWOGz6/jK7Nnz6Zx48ZZvowA69/upZdeYuzYsXmuYb///vupWbPmhYYJaMKplFJKFSki8hnwmTFmgIj84O94Shx7Dac79n50R49C3bqwb5/1uUYNa1tUlGPgoNADB3Jdu3no0CEOHDjgaBJqXTLCsS2z+++/HxHh77//dqzLaeTU6Oho1qxZ47J/mzZtXI7JqQbqrrvu4tSpU+zYscOxbs+ePYhIlutef/31zJo1i8mTJzvWDRo0iFKlSjk+e6rhjI+PJygoyDFCZ3auvPJKmjVrxsCBA7Pc7759+0hOTiYkJMSrOTjtYmJiOHLkCKdPn84yANPZs2c5ePBgnkeoBahfvz5t2rRh8ODBbrffeOONlClTxqt4r732WqZNm0br1jkPZN26dWsuvfRShgwZ4nb74MGDWbRokcu/2e233+5233bt2lG1alW+++47l/Pbv0RwVrFiRS699FKWL1/O8uXLc4zzQqWnpxMYGOjz6/jSww8/7Hb9Nddcw+TJk/nwww/zfO4bb7xRE06llFKqJBORH4wxfYBmQKjT+qf9F1UJcOwYVK3qfps94bQPHOQ8JQpYtZz2JrUHDlgj1+aCPdFzbuZYqlQpypcv77aGc+fOnZw4cYLjx49ToUIFl3N4SlJiYmI4ePAgZ8+eRUQ4dOiQI2mKiooiNDQ02z52x44dY8OGDYgIiYmJ1LDdu6emlFdffTXH7CP/ehAaGkpkZGSWRNc+QmfmZM+dqKgoR+2aM3s8e/bsoV69eiQkJNCxY8ccz+d87K5du2hibzJts3v3biDvI9SCdd8rV670uH3kyJGMHDnSq3M1a9aMjRs3erVvqVKlsq1hvPHGG7nxxhu9OlfdunXZZ//iJQfGmAKp2bRbsGABnTt3LrDrFaRLLrlER6lVSiml1IUxxrwPDALuBQxwHZD3v26Vd7xtUgtZE84mTawaThGrSW0ukxFPtZORkZFZ/ri095sTEZYsWZLjOezs63fv3p0laXI3yE5m9mau4P3AMt7IPPhMcnIyy5cv96o5aU7ntcd38uRJjh07lqsmtfZjM7vQOTiVKk404VRKKaWKpo4ichNwTEQmAB2AWn6OqfjztkktuK/hPHIEtm8n6OzZXDep9ZS0uUs4nZMg58FDMs+FmZlzEuUuacppoBx7M9cyZcpkuS6Q5yZ6mRPdFStWuB2hMy/ntceX21rJ7Obi9HZ6FaVKAk04lVJKqaLprO31jDGmOpAK1Mlmf3Wh0tMhKclzwmlf71zDGRJyvubT3uzy11+t11wmIwkJCYSHhzuax9pFRkZm6cNpT84yJ36Z58LMzDmJcpc05VTDGR8fT9u2benYsWOW61apUsVlhNjcsCe69tpT+7kzj9CZW/YE2NP9ZqdatWoEBgZ6rOE0xuRbHzilijJNOJVSSqmiaaYxpgLwMrAa2AlM82dAxZ59/klPTWqDgqzRaZ0Tzho1zk+hYh+p1p5w5qFJrbvRQKOiojzWcF5zzTWsWLGCc+fOuZzDkxo1amCMcdRwBgQEOPphWiHHOAbZyezs2bOsWLGCuLg44uLiWL9+vWPOztwMxuNOTEwMp0+fdvT3XLRoEU2aNHGM0ptXoaGhVK1a1aVG19s4g4KCqFmzpseEs3r16gQHB19QfEoVB5pwKqWUUkWQiDwjIsdtI9XGAI1F5H/+jqtYsw9u46mGE6xmtc5Nap2SNaKjISwM5s8//zkXPM3r6K5JbUJCAqVKlWLAgAGkpKSwYsUKIOeEMzg4mOrVqzsSsMxJk/MgO5mtWLGC1NRUYmNjiY2Ndek/mpvpRtxxbvqanp7O4sWLL7g5rfO57fcbFBREVU+DQnk41lOTWm1Oq5RFE06llFKqCDLG3G2r4UREkoEAY8xd/o2qmPM24cxcw2kXEACNGsGpU2QEB4Ntug9veUraIiMjOXv2LGfOnHHZt1atWlx22WWA1QTV01yYmcXExDiamGa+XnYD5Tg3c23fvj3BwcHEx8cjIvmWcCYkJLBhwwaSkpKIjY3N8/kyn9t+vzVr1szVVBmemhhfaI2uUsWJJpxKKaVU0TRSRI7bP4jIMcC7ORJU3thrLj01qbVvO3IERLImnODox3mucmUrAfXSmTNnOHz4sMeEE1zn4rQneBERETRt2pRFixZ5nAszM+cav8xJk/OorpnFx8fTrFkzIiIiKF26NG3atGHRokUcPnyYs2fPXnCTWvt17aPf5lcNp30EXE81yDkdu2fPHtLT0x3rMjIy2L17t9ZwKmWjCadSSilVNAUYp858xphAoJQf4yn+ctOk9tgxOHcua8Jp68eZnIfaTXDfv9A+4qxzs1rn2snY2FgWL17Mf//9B+Q8KE50dLRjWpTM+zoPsuMsPT2dJUuWuNQ6xsbGsnz5cv7991+vrpudqKgoQkJC2LVrF/Hx8dSsWTPfahCjo6M5d+4ca9euzXWM0dHRpKWlucw1eeDAAVJSUjThVMpGE06llFKqaPoV+NYYc4UxpiswFZjj55iKN3sNpzdNajNPiWJnr+GsUiVXl85uXkd7Dac94UxNTWXv3r2OhCwuLo4TJ07wyy+/ADkPihMTE0NKSgqpqalZrhcSEuIYZMfZunXrOHnypEutY1xcHCkpKfzwww8eY/eWfQ7QhIQE4uPjiY2NzTJ4Ul7Z4zp16lSeEk5wrfG90DlHlSpuilzCGXzihL9DUEoppQqDR4A/gFHA3cDvwMN+jai486aGs1IlazRbewLiqYYzjwmnp0GD4HzCuXfvXjIyMhzJkD0JnDp1KpDzXJjOSZe769n7eDqz9990TjjtU5bYr3uhCVhMTAzx8fHs3bs335rT2s/r7n1ujnV+HjoHp1Kuckw4jTF9jTGFJjENPXAAnDrFK6WUUiWRiGSIyHsiMlBEBojIByKSnvORKs+OHbNGmc1uLsmICOt1wwbr1V3COXAgRzp0yNWlExISCAgIoHr16lm2Ze7DmTnhiYmJoVatWuzfv9+ruTAzz7vpbnvmGs5FixYRHR3tsn9ERATNmjVj//79lC5dmkrZ9X31QnR0NPv37wfyr/+m/bzu3nujVq1agPsaTk04lbJ4k0gOBrYaY14yxjTxdUA5EoGFC/0dhVJKKeUXxphvba/rjTHrMi/+jq9YO3o0+9pNOJ9wrrP9U2ROEIOD4bvvONmoUa4uvWvXLmrUqEFQUFCWbRUqVCAwMNBRw+muNtTet9KbGrycavzsg+yICAAi4mjmmpnzdS+0Caw9lgoVKtCsWbMLOpezSpUqUaZMGSD3SWLZsmWpWLFiloSzXLlyVKhQId9iVKooyzHhFJFhQCtgO/CpMWapMeZ2Y0xZn0fnjjHw229+ubRSSilVCIyxvfYF+rlZlK8cO5b9CLVwfvv69RAVBaXyZxyn7KbZCAgIICIiwpFw2ms47bVvcL5G0JuEqnz58pQrV87xmpl9kB17jer27dvZv3+/21rH3Fw3J/ZzdOrUiYBcjPCbE3v/UOdr5EbmJsY6B6dSrrJ+TeaGiJwwxvwAhGEVdP2Bh4wxb4rIWz6ML4u0sDBNOJVSSpVkM4HWwLMicqO/g3GWmJhI7969/R2G10qVKsXLL79MgwYNsmz79ddfWb9+PQ8++OD5lceOQcWKpKamcscddziad7pISrJeN2yA8HDw8DyOHj3qsYlpy5Ytef75513WJSQk0CGbZriRkZEuNZyVK1cmLCzMsT23iV90dLTHGkn7OQYOHEh4eDgHDx50uYYzXySc+dmc1vncBw8edNR05vbYhQsXOn72ly5dSseOHfM7RKWKrBwTTmNMP+BWoB7wBXCJiBw0xpQGNgMFmnCmlykDmzbBnj2QQ6d3pZRSqhgqZYy5GehojLk280YR+dEPMQHW1BjOU3MUditXrqRFixZMmDAhy7aJEycSHx/PXXfdRenSpa2VR49C3bosX76cTz/9lCZNmhAeHu56YHKy9SpizbPp4XmcPHmSjIyMLOsPHTrE7NmzGTt2rKNvZnp6Onv27Mk2aYuMjHTUONrn4HTWtGlTRowYwYABAzyew9moUaM8buvYsSNdunTh1KlTnDt3joCAAAYNGkSTJll7XkVHR3P33XfTv39/r66bnXbt2jFgwAAGDRp0wefK7JZbbnHbJNgbN9xwA/v27XP87Ddo0IChQ4fmZ3hKFWne1HBeB7wuIn86rxSRM8aYW30Tlmdp9l/6c+fCLbcU9OWVUkopf7sTGApUIGsTWgH8lnBGR0ezfPlyf10+11q3bu0YXdVZSkoKf/31F2lpaSxbtowuXbpYG2w1nPZjFi5c6JgD0+H48fP9PAcNgg8+cHvtBQsW0Llz5yzr4+Pjueyyy1i8eDFXX301YM3rmJqamm3/y8jISLZs2QJYtaGZk7+AgAA++ugjj8dndtddd3ncFhUVxR9//OH1ud5++22v981O2bJl+f777/PlXJldSBI7aNAgnyTBShUX3vThvAn41xhzlTGmnzGmqtO2330anRsZISFQtao2q1VKKVUiicgiERkFPCwit2RaCvyL4KIsLi6Ov/76i5SUFJf1q1ev5uzZswCuCalTwtm4ceOsySZA+fIQGGi9zzxCrRfatWtHqVKlXK7rzTQbUVFRHD58GBHJtr+nUkoVNG+mRRkBLAeuBQYCf/mjZtNF9+5WDaebpihKKaVUcWaM6Wp7e8wYc23mxa/BFTFxcXGcPXuWNWvWuKxftGgRYA26Y39PSgqcPk1GhQosXrzYcz9CY84PHJSHhDM0NJRLLrnEJeHMbg5Ou8jISI4cOcKRI0c4ffq0DlqjlCo0vBni62GglYgMF5GbgTZYk037T/fucOQIZCoglFJKqRLgcturuxFq+/orqKLI3mcvc7Pa+Ph4GjRowNVXX83SpUtJS0uzajeBDefOkZSUlH1/P3vC6WbOTG/jWr16NadPnwbOJ5zOo85mFhkZSXp6Outs07FowqmUKiy8STj3ACedPp8EdvsmHC9162a9arNapZRSJYyIPGV7zdycVpvU5lLVqlWpX7++S8KZkZHBokWLiI2NJTY2llOnTrF27VpHwhm/dy+Qw0ip9rk481DDaT+3vf8oWE1qK1So4HaKEjv7AEOrVq0CvJtvUymlCoI3CWcisMwYM94Y8xTwF7DNGDPWGDPWt+F5ULkytGqlCadSSqkSyxgz2hhTzlg+MsasNsZ093dcRU1cXByLFi1yjBi7efNmjh49SlxcnCOpjI+Pt0aoBeL/+48aNWpQu3Ztzye9wISzY8eOGGMcibA3fTIzJ5xaw6mUKiy8STi3A/+HNfIdwE/APqCsbfGP7t1h8WI4dcpvISillFJ+dKuInAC6A5WBW4AX/RtS0RMXF8fRo0cdI7za+2zGxcVRvXp16tata607dgwB4jdvJi4uzuMclYCVcIaEnG9am0sVKlTg4osvdsSSkJCQYwJpH8Bo9erVhIWFORJQpZTytxynRRGRCQDGmLLWRykcGV737jBxIixYAH21y4pSSqkSx57x9AY+FZG/TbZZkHLHuR9n06ZNiY+Pp2rVqtSrV8+xffbs2cg117AT2HvoUM7zNQ4fDs2bWwMIXUBcU6ZMIS0tjV27dmXfhJfzNZxbt26lUaNG2SfESilVgLwZpba5MWYNsAHYaIxZZYxp5vvQctCpE4SFwf33Q8eO0KgRVKsGN9wAq1f7OzqllFLK11YZY37DSjh/tX0xrMO351L9+vWpUqWKo/lqfHw8sbGxjoQtLi6OQ4cO8e+WLdh7euaU/HH55fDAAxcUV1xcHKdPn+bPP//k+PHjXjepBW1Oq5QqXLxpUjsZGCsiMSISAzwAfOjbsLwQEmIlm+XKQXi41afzyith5kxo0wa6drXenzvn70iVUkopXxgBPAq0E5EzQDBWs1qVC8YY4uLiiI+PZ9euXVlqEx01oOvXE4/V3LV58+Y+j8sew1dffQXknESWLl2a0NBQr/ZVSqmC5E3CWUZE5ts/iMgCoIzPIsqN556DVauswYOmTYMvvoDdu+Hll+Hff6FfP6hQAbp0gQkTdBoVpZRSxUkH4B8ROW6MGQY8AST5OabC7b774LPPsqyOi4tj165dfP31147Pdo0aNSIqKopF27axKCCATp06ERDgzZ9PF8bef/T7778Hch511hjj6MepI9QqpQoTb35j7jDG/M8YU9u2PAH85+vA8qx8eXjwQdixA37+Ge6+G06ehKefhnbt4I8//B2hUkoplR/eA84YY1pgzZmdAHzu35AKscREeOsteDHruEr2Wsw33niDsmXLcvHFFzu2GWOsfpz//ceWjIyc+2/mo9jYWE6cOAF4V2tpb1arNZxKqcLEm4TzViAK+NG2RFIUmuyUKmXVcL76KqxcCQcOQOPGMHAgbN3q7+iUUkqpC5UmIgJcDUwSkUn4c/T4wu7//s963bLFWpy0aNGCsmXLcuDAATp27EhgYKDL9tjYWA7auujk2H8zH9mvFRwcTNWqVXPcXxNOpVRhlG3CaYwJBL4TkftEpLVtGSMixwoovvwTGWnVeAYEWIno8ePnt82bZ416a+snoZRSShUBJ40xjwHDgF9sZXawn2MqvKZPh+rVz793EhgYSMeOHQH3CaV9XUhAAG3btvVtnG6uW6tWLa+a8doTTm1Sq5QqTLL97SUi6VjNdcoXUDy+Vbcu/Pij1dx20CCr/2fPntCtGyxcCDfddP4bUKWUUqpwGwQkAyNEZD9QA3jZvyEVUkePWtOo3XwzXHJJloQTzjerdddktlWjRpQxhksqViQkJMTX0To0bNiQqKgor2sso6KiMMZQo0YNH0emlFLey3EeTuAcsN4YMxc4bV8pIvf5LCpfuuwyeO89uO02a7ChihWtZrfDh0OfPlYiOmeONdCQUkopVUjZkszXnD7vQvtwujdzJqSnQ//+ULYsjBsHe/ZAzZqOXUaMGEFqaiqdOnVyPTYlhaDBg3lXhOgxYwo0bGMM77zzDhUrVvRq/5EjR9KkSZMCTYqVUion3iScv9gWZ+KDWHzq3Llz7N+/n9q1a8OIEXDihPWN5wMPWCPZAvzyi5WQXnUVzJ8PBdhsRimllMoNY8ylwFtAE6AUEAicEpHi0SopP02fDjVqWOW6PeH8v/+De+5x7FKtWjUmTJjgelxaGgwdCrNnc9PkyTByZMHGDVx33XVe79u8efMCmbJFKaVyw5tBgyqIyGfOC+DdV22FyJ133knz5s05evSoteL+++GZZ84nmwCVKlm1npGRVlPbTIMKKKWUUoXI28AQYCsQBtwGvOPXiAqjM2fg11/hmmvAGGsAwcaN3TardZGRYbWG+v57qyWUH5JNpZQqDrxJOG92s254PsfhU+vWrePzzz/n9OnTfP55Dq2NqleHuXMhKMjq27l7d8EEmQdpaWlkZGT4OwyllFJ+IiLbgEARSReRT4HOfg6p8Pn1Vzh71mpOa9e/vzV2w5Ej7o8RgdGjrTk7x4+HsWMLJFSllCqOPCacxpghxpgZQB1jzM9Oy3zAw2/owumxxx6jfPnytGjRgg8++ABrFPls1K9v9eM8ccIavfbw4YIJNJeaN2/Oc8895+8wlFJK+ccZY0wpYK0x5iVjzP1AGX8HVehMn26N13DZZefX9e9v9emcOdP9MY8/Dm+/bXW7efLJgolTKaWKqexqOJcArwJbbK/25QGgp+9Dc2/btm0MHTqUL7/8kkOHDuW4/8KFC5k1axaPPfYYY8aMYcuWLcTHx2d7zLlz55i6eTPJP/wAO3dC795w8mQ+3UH+OHbsGP/88w8zPRWWSimlirsbsfpt3oM1qF8tYIBfIypsUlNhxgxrOrRgpxlj2ra1BgxyNzL9Cy9Yyx13wMsvW81wlVJK5ZnHhFNEEkRkgYh0EJGFTstqEUkryCCdhYeHM2/ePG688UaqVKlCo0aN6NmzJ3feeScTJ07kv//+c74HHnnkEWrUqMG9997L9ddfT/ny5fnggw+yvcZzzz3HDTfcwMQlS+Cbb2D1aqvvx6lTPr47723duhWA1atXc+bMGT9Ho5RSqqDZyumzInJCRCaIyFhbE1tlt3ChNe+2c3NasJLIa66xmts6l6FvvWUNKDR0KLz7riabSimVD3IcpdYYcy0wEagMGNsiIlLOx7G5VbVqVTZv3szq1auZNWsW69evZ+fOnaxatYrDhw/z5JNP8sADD/DYY48xd+5cli1bxscff0xYWBgAN954I5MnT2bSpEmOCZKdJSQk8Morr1CqVCkmTpzIbVu3Uv3TT61pUzp3tprfVK1asDftxrZt1t8UaWlprFixgssvv9zPESmllCoIxpj1ZDNavIhcXIDhuAjfutUaBbawSEmBsDCre0xm11xjNZuNjITAQGvdqVPW+ilTIMCbYS6UUkrlxJtpUV4C+onIZl8H462AgADatm1L20zTluzZs4dx48bxwgsv8OmnnxIcHEzTpk256aabHPvccccdvP3220yZMoUHH3wwy7kfeeQRjDH89ttvdO/enSeeeIJPPvnE6v8xaBB06ACzZ1sj3PmRvYYTYPHixZpwKqVUydHX3wF4klqhAtxyi7/DcNW+PZQunXV9585W01nn7jlRUdYo9kHe/HmklFLKG978Rj1QmJLN7NSsWZPPP/+cu+++m9GjR7Ns2TJ+/vlngpwKjubNm9OxY0cmT57MAw88gHFqLrN48WK++eYbnnzySS6//HJGjx7NK6+8wj333EPrvn1hwQLo2xc6drS+/ezd22+F0rZt26hVqxbh4eEsXrzYLzEopZTyi2Cgioi4/PI3xsQBe/0TkiU5KsqaQqQoCAyERx/1dxRKKVXsedNeZKUx5hvbqLXX2hefR3YB2rdvz5IlS9ixYwf9+vXLsv2OO+5g69atzJ8/37EuIyODMWPGUKNGDR5++GEAxo0bR0REBA888IA1sm27drB0qfUN6NVXW1Oo3Hkn/P67NdpdAdq6dSsNGjSgU6dOLFmyRKdHUUqpkuMNwN1Idmdt25RSSqlCw5uEsxxwBugO9LMthbY5j11AQAB16tRxu+26666jYsWK3HrrrTzwwAPMnj2byZMns3LlSl588UXKlLFGla9QoQITJkxgwYIF/Pzzz9bBdevC2rXwww/QtSt8+SVceaX1fm/BfbG8bds26tevT6dOnTh+/DibNxeJSmillFIXrraIrMu8UkRWArULPhyllFLKsxwTThG5xc1yqzcnN8b0NMb8Y4zZZoxx227FGNPZGLPWGLPRGLMwtzeQF2FhYXzxxRfUq1ePt99+m969ezNq1CguueQSbrjhBpd9b7/9dpo0acK9997LTz/9ZNUkhoXBtdfCtGlw8CBMngwrV0LLltaIdz527Ngxjhw54qjhBLRZrVJKlRyh2WwL8+YEhbV8VkopVfx4TDiNMd86vZ+YadtvOZ3YGBMIvAP0ApoCQ4wxTTPtUwF4F7hKRJoB1+Um+AvRp08ffv/9d44dO8Zvv/3GU089xWeffUZAplHpgoKC+OSTTwgODuaaa66hRYsWTJs2jXR7E9rSpWHkSCvhrFKFPT178mKXLiQlJPgsdvuAQfXr16d+/fpERUVpwqmUUiXHCmPMyMwrjTEjgFU5HVzYy2ellFLFS3Y1nA2c3nfLtC3Ki3NfAmwTkR0ikgJMA67OtM8NwI8isgtARA56cd58Vbp0abp168b48eNp7GHk2UsvvZR//vmHL7/8kvT0dIYMGULTpk35/PPPSUuzpiTNaNSId269labBwTy2YAH31qljNbOdNAl2787XmO1TojRo0ABjDJ06ddKEUymlSo4xwC3GmAXGmFdty0LgNmC0F8cXifJZKaVU8ZBdwulxjq8cttnVAJwzrT22dc4aAhVtheYqY8xNFFJBQUEMHTqUDRs28P333xMWFsbNN99Mo0aNmDRpEnFxcdwzdiyXdu7MqAED+EKEn7dtgzFjoFmzfO3fuXXrVowx1KtXD4BOnTqxfft2Dhw4kG/XUEopVTiJyAER6QhMAHbalgki0kFE9ntximJVPiullCrcspvTo7QxphVWUhpme29sizd9RIybdZkT1SCgDXCF7ZxLjTF/ici/Licy5nbgdoCoqCgWLFjgxeV9JyIigtdff50lS5bw+eefM2bMGMqVK8djjz1Gt27dSEtL49c1a7jl+HG+fe01uj74IIn33ss/d93Ft99+S2hoKP3798/z9RcvXkxUVBR//fUXYPVJBfjggw+47LLLHPudOnXK78+qqNBnlTv6vLynz0r5iojMB+bnuGNW+VY+Q+Ero4sK/d3gPX1W3tNn5T19VgXHiLivrDTGZFuIiUiXbE9sTAdgvIj0sH1+zHbcC077PAqEish42+ePgTki8p2n8zZq1Ej++eef7C5doESE1atXU7t2bSIiIhzr165dS7t27Rg0aBBflirFnq+/5oaWLYlftozAwEA2bNjgsQlvTi699FLKlCnD77//DkBycjLly5fn7rvv5lWn+c8WLFhA586dL+j+Sgp9Vrmjz8t7+qy8Z4xZJSJt/R1Hceer8hkKXxldmOnvBu/ps/KePivv6bPy3oWWzx6b1IpIl+wWL869AmhgjKljjCkFDAZ+zrTPT0CcMSbIGFMaaA8Uqfk9jDG0adPGJdkEaNmyJU888QRfffUVDwcF0SI5mdWrV/PWW28RFhbG448/nudr2qdEsQsJCaFdu3baj1MppZQ3SkT5rJRSqnDwZh7OPBGRNOAe4FesQupbEdlojLnTGHOnbZ/NwBxgHbAc+EhENvgqpoI2btw4WrZsycsffkhMpUqsNoZ7Bg7koYce4scff3Q0ic0N5ylRnHXq1InVq1dz9uzZ/ApfKaVUIZZ5BHlP6zLT8lkppVRB8lnCCSAis0SkoYjUE5HnbOveF5H3nfZ5WUSaikhzEXnDl/EUtODgYH788UfefPNNlv75Jw3T0+Hllxk7diyVK1fmkUcewVOTZk/sI9Q613CClXCmpqayfPnyfItfKaVUoZZ5BHmwpjrJUUkvn5VSShUcnyacCurUqcO9995LSLNmMHQovPce4adP89RTT/Hnn38ye/bsXJ3PPgdn5hrOuLg4AgMDmTNnTr7FrpRSqvAxxowyxqwHGhtj1jkt/wHr/R2fUkop5cxjwmmMaZ3dUpBBFhtPPAHJyfDoo4y88Ubq16/Po48+Snp6utenyDwlil2FChWIi4tjxowZ+R21UkqpwuVroB9WP8t+TksbERnqz8CUUkqpzLKr4Xw1m+UV34dWDDVoAPfdB1OmENysGc9deSXr16/n1ltvZd68eaSkpOR4im3btlGzZk1CQ0OzbLvqqqvYuHEj//33ny+iV0opVQiISJKI7AQmAUdFJEFEEoBUY0x7/0anlFJKucrrKLVdCzLIYuX11+H336FaNQa+/z4jypfn22++oVu3bkRFRTFkyBASExM9Hr5169YszWnt+vXrB6C1nEopVTK8B5xy+nzatk4ppZQqNLzqw2mMaW6Mud4Yc5N98XVgxVrXrvDXXwT8+CMflSvHkSpV+GnaNK6//npmzJjBFVdcwYEDB9wemnlKFGf169encePGmnAqpVTJYMRp5DkRyQCC/BiPUkoplUWOCacx5ingLdvSBXgJuMrHcRV/xkD//jB1KqV37+aqhQv58MMPmT17Nrt27aJbt24cPXrU5RBPU6I469evHwsWLCApKcnXd6CUUsq/dhhj7jPGBNuW0cAOfwellFJKOfOmhnMgcAWwX0RuAVoAIT6NqiTp1Anuvx/eew/++IO4uDh++ukn/vnnH3r27MmJEyccu3qaEsVZv379SEtL49dff/V56EoppfzqTqAjkAjsAdoDt/s1IqWUUioTbxLOs7ZmOmnGmHLAQaCub8MqYZ55xhpQaMQIOHWKbt268f3337NmzRp69OjBunXrAM9Tojjr0KEDERER2qxWKaWKORE5KCKDRaSyiFQRkRtE5KC/41JKKaWceZNwrjTGVAA+BFYBq4HlvgyqxCldGj79FBIS4JFHAKumcurUqWzatIkWLVowYMAA5syZ43ZKFGdBQUH07t2bWbNm5Wq6FaWUUkWLMaahMeZ3Y8wG2+eLjTFP+DsupZRSylmOCaeI3CUix0XkfaAbcLOtaa3KT506wZgx8O67sHAhAAMHDmTnzp08+eSTzJs3jy+++MJ1SpQXXoA//8xyqn79+nH06FE2btxYgDeglFKqgH0IPAakAojIOmCwXyNSSimlMvFm0KD+xpjyALZ5v3YZY67xcVwl07PPQu3aMGoU2ObkrFixIhMmTGDnzp08/fTTjB8/3tp3xw4YNw5eey3LaXr06EFwcDBLliwpuNiVUkoVtNIikrnFUZpfIlFKKaU88KZJ7VMi4hjyVESOA0/5LKKSrHRpePtt2Lw5SyJZsWJF/ve//3HrrbdaK775xnpdsgTOj4oPQLly5ejcuTNLly4tiKiLvNOnT/PVV1+Rmprq71CUUio3Dhtj6gECYIwZCOzzb0hKKaWUK28STnf76DxfvtKnjzVdytNPw86dnvf75hsICIBDh8A2mJCz/v37s2vXLubMmeO7WIuJWbNm8dFHH/Gnm+bJKnfWrl3L7Nmz/R2GUiXF3cAHQGNjTCIwBmvkWqWUUqrQ8HbQoNeMMfWMMXWNMa9jDR6kfGXSJCuZvO8+99s3b4a//4bbbaPfL16cZZdbb72V6OhoRo0axenTp30YbNGXmJgIwI4dOn3dhXrmmWe4/XadlUEpXzPGBAKjRORKIApoLCKxIpLg59CUUkopF94knPcCKcA3wHfAOaxvVZWv1KoF48fDjBnw009Zt9trN//3P6hUyW3CGRISwoMPPugYdEh5pgln/klMTGTfvn06QrJSPiYi6UAb2/vTInLSzyEppZRSbnkzSu1pEXlURNqKSBsReUxEtMrM10aPhubN4d57ISnp/HoRmDYNOneG6tWhY0e3CSfARRddxB133MEbb7zBqlVaKe3J3r17AU0484M92Tx4UKcCVKoArDHG/GyMudEYc6198XdQSimllDOPCacx5g3b6wxbgeayFFiEJVVwMHz0EezdazWdtQ8M9Pff8M8/MGiQ9blTJ9iyBQ4fdnuaF198kSpVqjBy5EjS0nTwQne0hjN/iAj79lnjldifqVLKpyoBR4CuQD/b0tevESmllFKZZFfD+YXt9RXgVTeL8rX27eG55+Dbb2HyZGvdtGkQFATX2r7E7tTJevUwBUqFChV46623WLNmDa+5mUJFnU+Otm/f7udIirYjR444Rvr1lHCuXbtWm9sqlQ9sfTgPi8gtmZZb/R2bUkop5cxjwikiq2wF2kgRWZh5KcAYS7aHHoIePawmtn//bSWc3bpBZKS1vW1bqzbUQ7NagGuvvZb+/fvz2GOP8e233xZQ4EWDiLB3716Cg4M5duwYx44d83dIRZa9dhPcJ5w7d+6kdevW+jOoVD6w9eFs7e84lFJKqZxk24fTVqBFGWNKFVA8KrOAAPj8c6hY0Uo0ExJg8ODz28PCoE2bbBNOYwxffPEFnTp14oYbbuD7778vgMCLhqSkJM6cOUOjRo0A+O+///wcUdHlnHDa+8U6+/fffxER/vnnn4IMS6nibK324VRKKVXYeTNK7U5gsTHmf8aYsfbFx3EpZ5Urw1dfWf00Q0Lg6qtdt3fqBCtXQnKyx1OUKVOGWbNmcemllzJ48GB+/PFHHwddNNhr4i666CJA+3FeCHuSGRAQ4LaGMyHBmq1h165dBRqXUsWY9uFUSilV6HmTcO4FZtr2Leu0qILUtSu88w488wyUL++6rVMnK9nMYSTa8PBwZs+ezSWXXMKgQYOYOHEiBw4c8GHQhZ8mnPnHXsPZrFkzj01qQRPO4iQhIYFNmzb5O4wSy03/Te3DqZRSqtDxZlqUCSIyAXgNeNXpsypoo0ZZfToz69jRes2mWa1d2bJlmTNnDl27duXRRx+lRo0a9O3bl++++65EDuZir5WLiYkhMjJSE84LsG/fPsqXL0/9+vXdNqm113DaX1XRd8stt9CzZ0/EPoq2KlDGmJrGmOnGmIPGmAPGmB+MMTX9HZdSSinlLMeE0xjT3BizBtgAbDTGrDLGNPN9aMprVapA/fpeJZwA5cqV49dff2XTpk089NBDrF27luuvv56uXbuye/duHwdbuNhr4iIiIqhbt66OVHsB9u7dS7Vq1ahevXq2TWp3795NRkZGQYen8tmJEyeIj49n9+7drFu3zt/hlFSfAj8D1YEawAzbOqWUUqrQ8KZJ7WRgrIjEiEgM8ADwoW/DUrnWqZM1NUouahqaNGnCCy+8QEJCAlOmTGH16tW0aNGC6dOn+zDQwiUxMZFKlSoREhJC3bp1tYbzAuzbt4/q1atTo0YNjh8/zpkzZ1y2JyQkYIwhOTmZQ4cO+SlKlV/++OMPx9y+M2fO9HM0JVaUiHwqImm2ZQoQ5e+glFJKKWfeJJxlRGS+/YOILADK+CwilTedOsGhQ7B1a64PDQwM5Oabb2bNmjXUq1ePa6+9luuuu46bbrqJbt260bx5cwYOHMjp06d9ELh/7d27lxo1agBQt25dEhISHH9Eq9zZt28f1apVczxP52a1qampJCYm0qJFC0Cb1RYHs2fPpmzZsrRs2VITTv85bIwZZowJtC3DsAYRUkoppQoNbxLOHbYRamvblicAnTuisLn8cuv1Av7wq1+/PosXL+ahhx7it99+488//+TUqVPUqVOH6dOnc9VVV3H27Nl8CrhwSExMdEk409PTS1yz4vxgn8/U3qQWXOfi3LNnDxkZGcTFxQE6cFBRJyLMmTOHK6+8kv79+7Ns2TIOHjzo77BKoluB64H9wD5goG2dUkopVWh4k3DeitVE50dguu39Lb4MSuVBw4Zw6aXw4Ye5alabWalSpXjppZdISkpi586dLF26lBkzZvDZZ58xf/58rr32WpKzmX6lqElMTHQkSHXr1gWyjlR77NgxkpKSCjy2ouT48eMkJyc7mtSCa8Jpr9HUhLN42Lx5M7t27aJnz5707dsXEWH27Nn+DqvEEZFdInKViESJSGURuUZEtPmAUkqpQsWbUWqPich9ItJaRFqJyGgROVYQwalcGjkStmzxevCg3Bg2bBiTJ09mzpw5DBo0iNTUVK+O27ZtW6H9QzQtLY0DBw44EqR69eoBZBk4qGfPngwbNqzA4ytK7FOiODepdU447VOitGzZkvDwcG1SW8TNmTMHsP5vtGrViurVq2dpVrt79266d+/OsmXL/BFisWaMeckYc6eb9fcbYyb6IyallFLKk6CcdjDGzAAyV5klASuBD0TknC8CU3kwaBCMGQOTJ0NsbL6f/rbbbiM5OZl77rmHihUrUqVKFapUqULNmjV57rnnaNCggcv+qamp9OvXjy1btnDrrbfy9ttvExYWlu9x5dWBAwfIyMhwJEg1atQgODjYpYZzx44dLF++nIoVKyIiGGP8FW6hZu+vWa1aNcqVK0d4eLhLH057ghkdHU1MTIzWcBZxc+bMoWnTpkRHRwPQp08fpk2bRkpKCqVKlQJgzJgxzJ07l//++4+///6b0qVL+zPk4qYv0NzN+knAOuCRgg1HKaWU8syrPpzAKayRaT8ETgAHgIboaLWFS5kyMHQofPcdHPNNJfTdd9/Njz/+yO23386ll15KmTJlmDNnDkOGDMky2M7bb7/Nli1bGDBgAJ988gkdOnRg27ZtPokrL+w1cPYmtYGBgdSuXdsl4bSP2Hvs2DH++0+7Lntir+G0P8vMU6MkJCRQrVo1QkJCiI6O1oSzCDt9+jQLFy6kZ8+ejnV9+/bl5MmTxMfHAzBr1ix+/PFHrr32WrZt28bjjz/ur3CLKxGRLHML2dbpt2JKKaUKFW8SzlYicoOIzLAtw4BLRORuoLWP41O5dfvtcO4cfPmlzy7Rv39/XnvtNb766ivmzp3LJ598wqpVq3jppZcc+xw4cIDx48fTq1cvvvvuO3755Rd27dpFmzZtmDt3rs9iyw17QmSv4QSyTI0yffp0ypUrB8CqVat8Gk9ycnKRbWrq3KQWrGeaOeGMiYkBrFrOonqfJc25c+d48803OXDggGPdggULSElJcUk4r7jiCkJCQpg5cyZnz57lnnvuoXHjxkydOpW77rqLSZMmsWjRIpdzp6SkFKovoIqYM8aYBplX2tYVr5HdlFJKFXneJJxRxpho+wfb+0jbxxSfRKXyrlUraNPmggcPyo2BAwdy/fXXM378eDZs2ADAuHHjOHv2LK+//jrGGHr37s3q1aupWbMmw4cPzzJHoz/Ym3x6Sjj379/PkiVLuOeeewgODmb16tU+jeeNN96gadOmRXL6mb179xIeHk54eDhgPVPnJrU7d+6kdu3aAMTExHDkyJEieZ8lzdNPP83o0aPp1KmTo4Z/zpw5lC5d2jEAFECZMmXo2rUrM2bM4Pnnn+e///7j3XffpVSpUkycOJGYmBhuvfVWzpw5Q2pqKh9++CENGzakd+/eOg1R3jwJzDbGDDfGXGRbbgF+sW1TSimlCg1vEs4HgEXGmPnGmAVAPPCQMaYM8Jkvg1N5dPvtsH495TZvLrBLvv3221SoUIHhw4ezZMkSPvnkE8aMGUOjRo0c+9SuXZv33nuPvXv3MmnSpBzP+dtvv/HFF1/4LObExESCgoKIijo/T3rdunU5duwYx44d46effkJEGDx4MM2bN/d5DeeSJUs4c+aMI2kvSvbt2+doTgtWk9q9e/ciImRkZLB7926XGk5Ap58p5NatW8fLL79Mt27dOHr0KJ06dWL9+vXMnj2bLl26EBoa6rJ/37592b59Oy+88ALDhg2jS5cuAISHh/Pxxx+zdetWrrvuOho2bMjtt99OlSpVePPNNwkMDPTH7RVpIjIbuAboAkyxLZ2BASIyy19xKaWUUu54M0rtLKABMMa2NBKRX0TktIi84dPoVN4MGQJlylCtACdjj4qK4r333mPVqlX06NGDqlWr8sQTT2TZ77LLLqNfv368+OKLHD582O25Tp8+zZ133kmPHj0YPny4o7lmfktMTKRatWoEBJz/b+A8Ncr06dOpX78+zZs3p3Xr1qxatQrxYa2xvQb177//9tk1fMU+B6ddjRo1SElJ4fDhw+zbt4/U1NQsCacvm9UmJCRw6NAhn50/P6Snp3PMR32tL1R6ejojR46kYsWKTJ06lfj4eIwxdOrUie3bt7s0p7Xr06cPYCWYr7zyisu2rl27MmrUKGbNmkVkZCS//PILf/31Fz179tSBuPJIRDaIyM0i0sa23Cwi6/0dl1JKKZVZjgmnMaY08BBwj4isBWoZY/r6OjB1AcqWhSFDqDJvHvTpAxMmwKxZcPKkTy87YMAABg0axKlTp5g4caKj72NmL774IqdOneK5557Lsm358uW0atWKyZMnM3z4cDIyMpg2bZpP4t27d69Lc1o4PzXK6tWr+f333+nfvz/GGNq0acPRo0d9liQdPnyYPXv2AEUz4dy3b1+WhBOspN4+JYpzk1rw3VycqampdOzYsdBPZfPEE09Qt25djhw54u9QsnjnnXdYvnw5b7zxBhERETRr1ozFixdTtWpVAHr16pXlmJiYGEaPHs1HH31ElSpVsmyfNGkSq1atYvny5fTu3VsTTaWUUqqE8KZJ7adYfTU72D7vAZ71WUQqfzzzDAeuvBISEqyEs08fuPRS8HF/qQ8//JCffvop2z/2mzZtyi233MI777zj6C956tQpHnnkETp27Mi5c+f4448/+PTTT2ndujVfffWVT2JNTEzMknDWqVMHgLfeeou0tDSuvfZaANq0aQPgs36ca9asASA0NLTIJZwikqVJrf257t2715Gk2xPN6tWrExAQ4LOEc+bMmezdu5e5c+c6kvjC5uTJk7z77rscP37cq+blBWnXrl2MGzeOnj17MmTIEMf62rVrs3TpUhYuXOj4YiazN954g4EDB7rdFhwcTOvWrTXRVEoppUoYbxLOeiLyEpAKICJn0WHXC7+qVfnn4YdhwwZISoL33oNNm8BHyZtd2bJlueqqq1yaqbozYcIEgoKCeOKJJ/jhhx9o0qQJL730EjfffDPr1q2jc+fOAAwbNoxVq1axZcuWbM+3fft2Hn/8cX788UdOnTrlVayJiYkuSRJAuXLliIyMZP369VSrVo1LLrkEgIsvvpigoCCf9eO0J7LXXHMN69atIyMjy4wHhdaJEyc4c+aMSw2n/bkmJiZmSTiDgoKoUaOGz2qLP/zwQypVqoSI+OzLCrszZ86wdevWXB/3ySefcOLECS666CLefPNNkpKS8iWe9PR0ZsyYweDBg3nyySc5ePBgln3279/PV199xUMPPUS3bt2IiooiLCyMunXrEhsbS48ePRAR3n333SzJYUREBJdddlm+xKqUUkqpksGbhDPFGBMGCIAxph6Q7NOoVP4qWxbuuMMawfaZZ3xey+mNGjVqcP/99zN16lQGDhxIREQEixcv5uOPP6ZChQqO/QYPHkxAQEC2icOiRYto3749zz//PAMGDCAyMpI+ffo45tB059SpU5w4cSJLDSec78fZv39/R+IcGhpKs2bNfJZwrlmzhpiYGLp06cLJkycdzVDdOXnyJIsXL2bq1Kmkpqb6JJ7cyDwliv29McaRcEZERFCmTBnH9piYGJ/UcCYkJDBnzhzuvvtuOnXqxGeffeazfrciwtVXX02zZs1y/ELEWXp6OpMmTaJTp05MmTKFpKQk3nnnnSz77du3z+t+qAcPHuT555+nbt26XHXVVcybN49nn32W6Oho7rjjDv766y/efvttLr/8cqpXr86wYcN46623OHbsGFdffTV33303HTp0IDg4GGMMb7zxhqO2XxVexpgoY8w4Y8xkY8wn9sXfcSmllFLOvEk4xwNzsPpufgX8Djziy6CUDxgDTz0F27f7vJbTWw8//DD9+vXj9ddfZ+XKlXTs2DHLPtWqVeOKK67gq6++cps4fP3111xxxRVUqlSJTZs2MX/+fEaNGsWmTZsYMGAAS5cudXttd1Oi2DknnM7atGnjs4GD1qxZQ6tWrWjRogVgjRCa2ZgxY2jQoAHlypUjNjaWG264gU8//TTfY8mOiDBt2jTOnTvnWGdPOJ1ri4ODg6lcuTJ79+51mRLFLjo62icJ5yefWH9rjxgxgptuuonNmzdn+ZIgMTGRxx57zGXalrz4+OOPmTdvHhkZGYwaNcrrn4uffvqJ//77j7Fjx9K6dWt69+7Na6+95jJNzE8//UR0dDSVK1emSZMmjBw5ks8++4z9+/e7nOv48eM8/vjj1KlTh8cff5wGDRrwww8/sG/fPjZv3szNN9/MZ599RocOHbj33ns5fPgwTz75JGvWrOHUqVOsXLmSjz76iFdeeYWvvvqK+fPns2nTJkaOHHlBz0YVmJ+A8sA8rClR7ItSSilVeIhIjgsQAfQB+gKR3hzjq6Vhw4aivDN//nzXFRkZIi1bitSrJ5Ka6peY8mLKlCkCyOLFix3rMjIyZPz48QLI5ZdfLkeOHHE55uTJkxIdHS1NmjSRc+fOZTnnH3/8IYD8/vvvIuL6rN566y2pV6+epKSkuBzz9ttvCyAJCQn5eHciJ06cEGOMTJgwQU6dOiXGGBk/frzLPtu2bRNAYmNj5ZlnnpEZM2ZIkyZNpEOHDvkaS05++eUXAeSOO+5wrPvyyy8FkM2bN7vs26pVK+ndu7c0btxYrr32Wpdtjz76qAQFBUlaWlq+xZaamio1atSQnj17iojIsWPHJCQkRO69917HPunp6dK1a1cBJDIyUmbPnp2na+3evVvKlSsnXbp0kffee08A+eyzz9zum/n/YadOnaROnTqOe1+yZIkA8uqrr4qIyIwZMyQ4OFguueQSefHFF6VPnz5SoUIFwWplIm3btpWnnnpKnn32Wcf6wYMHy6ZNm9xe/8CBA/Lpp5/Kxo0b83SvBQlYKX4sX4raAqz1dwyZFy2jvZeljFYe6bPynj4r7+mz8t6Fls/eFGi/e7OuoBYtzLzn9j/S//2f9c/u4Y/jwigpKUnCwsLkrrvuEhGRlJQUGT58uAAyfPhwSU5Odnvc7NmzBZD//e9/WbZ98cUXAsiWLVtEJOuzysjIyHLM0qVLBZDp06df2A1lEh8fL4DMmDFDREQaNmwo/fv3d9nnrbfeEkC2bt3qWPfSSy+53ENBGDhwoADSoEEDx7qXX35ZADl+/LjLvn379pUWLVpIWFiY3H///S7b7Enanj178i22n3/+WQD58ccfHeuuv/56iYiIcPyM2L80GDdunDRv3lwAeeSRR7J8uZCdjIwM6dWrl5QuXVq2b98u6enpcumll0pkZKTLFx9LliyRtm3bysCBAx3PZtmyZQLI66+/7nLOrl27StWqVWX69OlSqlQpadu2rRw7dsyxPT09XdasWSPPPfecdOjQQYwxAkifPn1kzZo1uX9YhZQmnLkswK0B/Hr7Ow7nRcto7+kfu97TZ+U9fVbe02flPZ8lnEAoUAn4G6hoe18JqA1svpCLXsiihZn33P5Hstdy1q9fpGo5Bw0aJBEREXL06FHp0aOHADJ+/Hi3iaGzYcOGSVBQkKxbt85l/cSJEwWQkydPioh3v3TOnDkjgYGB8sQTT+T5Ptx58803XZKv6667TurWreuyT69evVySPBGRvXv3SkBAgDz22GP5Go8nhw8fllKlSknlypUFkH/++UdERMaOHSulS5fO8m9xxx13SKlSpQSQSZMmuWyz15QuWbIk3+Lr16+fVKlSxSV5nDlzpgDyf//3f/Lvv/9K6dKlpWfPnpKRkSFnzpyR22+/XQBp376914mbvcbd+Z7Wrl0rgYGBMnLkSElNTZWnnnpKAgMDpXLlymKMkcqVK8snn3wigwYNknLlyklSUpLLOe017oC0bNkyS419ZgcPHizQLxoKiiacuSzA4SSQAZyzvT8JnPBnTFpGe0//2PWePivv6bPynj4r711o+ZxdH847gFVAY9urffkJyDrChSoa7H05t22Dr7/2dzReGzZsGEeOHKFZs2bMmzePjz76iKeeeirHKRZef/11KlasyG233UZ6erpjfWJiIuXKlSM8PNzrGMLCwmjatGm+Dxy0Zs0aoqKiHH0gW7RowY4dOzhx4gRgjYQ6f/58evfu7XJctWrV6NmzJ1988YXLvfnK1KlTSUlJYcqUKRhjHPOj7t271zFIkLMaNWqQkpICnB+h1i46Ohog30aq3bNnD7/88gu33norwcHBjvXdu3encuXKfPrppwwfPpxSpUrx0UcfYYwhLCyMDz74gGnTprF9+3batGnDvffey/Hjx91eQ0SYN28eY8aMITY2lnvuucexrUWLFowZM4YPP/yQtm3bMmHCBIYMGcK///7L+++/T/369bn11lv55ptvuO2227LMUdu5c2e6detG69atmTdvHpUqVcr2fqOiomjUqFHeH5gqFkSkrIgEiEio7X1ZEXE/AbJSSinlJx4TThGZJCJ1gAdFpK6I1LEtLUTk7QKMUeW3q6+Gli3h6acLxYi13ujRoweRkZEkJSXx888/M2LECK+Oi4yM5M0332T58uU8//zzjvXupkTxxoUMHJSSksKoUaNYsGCBy3r7gEH2hO3iiy8GYP369QAsWLCAc+fOZUk4AYYPH86ePXv4448/vI7j9OnTjB8/no0bN+Yq/k8//ZRWrVrRq1cvLrroIqZOnYqINQen8wi1ds7P11PCmdeBg7788kuqVatG5cqVqVWrFm3btiUjIyPLz0VwcDBDhw7lp59+YsmSJbz99ttZBooaNGgQ//77L6NGjeLdd9+lYcOG/O9//+P7779ny5YtnDlzhs8++4yWLVvSrVs3wsLC+Pjjj7NM/TN+/Hiio6PZuXMnU6dO5YsvvqB8+fI0bNiQRYsW8fnnn9OjRw/Gjh2b5X6MMcyaNYuVK1cSERGRp2eiSiZjzFXGmFdsS19/x6OUUkplluMotSLyljGmuTHmemPMTfalIIJTPmIMTJhgjVj7xRf+jsYrwcHBzJ07l9WrV7tNvLIzaNAgx7yEd955JykpKSQmJrodoTYnrVu35uDBg3ka4XTcuHG8//77jBgxwlHzl5yczIYNG2jdurVjP/tItX///TcAs2bNonTp0lx++eVZztmvXz8qVqzIlClTvI7jgQceYMKECbRq1Yrx48eTnJzzLEfr1q1j9erV3HLLLQBcccUVbNmyhfXr17Nv3z63ybvz882ccJYrV44KFSrkKeH84osvuOmmm4iJiWHAgAF069aNK664gokTJ1KvXr0s+990k/Xr6tprr+WGG25we86KFSvy9ttvs2rVKpo2bcrzzz/PddddR5MmTShTpgzDhw8nIyODTz75hB07dtCwYcMs5wgPD2fFihVs27aNwYMHu2wzxnDjjTcyZ84cjz93QUFBOdbYK+XMGPMiMBrYZFtG29YppZRShUZQTjsYY54COgNNgVlAL2AR8LlPI1O+1a8ftGlj1XIOGwZOzRALq5YtW+bpOGMMX375JXXq1OGFF15g48aN7Nixg549e+b6XG3atAFg8uTJXHTRRaSmppKSksKxY8c4evQoR48eJTIykkceeYSwsDDHcb/++iuvvvoqHTp0YOnSpXz44YfcfffdbNy4kbS0NFq1auXYt1atWlSoUIF169YhIvzyyy9ceeWVhISEZIknNDSUwYMH8+mnn5KUlET58uWzjX/mzJl88MEHjBo1iqSkJCZMmMB3333H+++/T2xsrMeEZ8qUKQQHBzsStssuu4w333yTadOmsXfvXnr16pXlGHtiZU8uM4uOjs62SW16ejrp6emUKlXKse6LL77g5ptvpmvXrvz888+ULl062/sF6+dmzpw5dOjQIceErmXLlixYsICzZ8+yZcsWNmzYwL///ktcXBzdunXL8fjKlSvnGI9S+ag30FJEMgCMMZ8Ba4BH/RqVUkop5SynTp7Aeqya0L9tn6sAMy6k4+iFLDoggfdy7Az9yy8iIPLBBwUST2EwdepUCQsLE0AeffRRx3pvO46fPn1aSpcu7RjgxXkxxkjFihUFkHbt2jkGAdq/f79UrlxZmjVrJmfOnJHLLrtMKleuLCdPnpSPPvpIAPn3339drnP55ZfLpZdeKps2bRJA3n//fY8x2Uc+nTx5smRkZMiff/4pt99+u4wdO9ZlcJqDBw9K5cqV5eKLL3ZMFTNz5kypVauWAFKlShUZMmSIfPjhhy6jx6akpEhUVJQMGDDA5Xn16NFDqlevLoC8+OKLWeI6fPiwAHLRRRe5jbtfv35y8cUXu922YsUKqV69uoSFhUnXrl1lwoQJ8tJLL4kxRrp27SqnT5/2+DwKGx2UwHvooEG5WoB1QCWnz5WAdf6MScto7+nvBu/ps/KePivv6bPy3oWWzzk2qQXOivXtaZoxphxwEKh74amu8rtevaB9e3j2WfCiWWVxMHjwYBYvXkynTp3yVMNZunRptmzZwpo1a1i/fj2bN29m27ZtHDlyhNTUVI4ePcr//d//sXnzZtq2bcvixYsZPnw4J06cYNq0aYSFhTFx4kQOHjzI66+/zurVqylbtmyWpqAtWrRg/fr1zJw5E8BtDaJdu3btaNKkCc899xz16tXjsssu46uvvuKNN97goosu4vfff0dEGDlyJMePH+fLL7901Jb26dOHjRs38tFHH3HFFVcwf/58Ro4cSUxMDEOHDmXNmjXMmjWLQ4cOOZrT2g0ZMsTRtNhdk9pKlSoREhKSpTmtXXR0tNsmtdOnT+eyyy6jVKlS3HbbbRw5coTx48fz8MMP06VLF2bMmOFVzaZSJcALwBpjzBRb7eYq4PkcjlFKKaUKVk4ZKfAuUAG4E9iK1Vzn0wvJci9k0W9PvefVNze//SYCIu+84/N4CrP8/pZrw4YNUq9ePcd8ie+++67L9muuuUbKli0rTZo0kbi4uCzHf/zxxwJInTp1pHnz5jle78033xRjjFx55ZXy+eefy6lTp2Tp0qXSsGFDAeTKK68UQF599dVsz5ORkSEbN26UsWPHSnh4uABSvnx5qVq1qqQ6TaMzf/58OX78uISEhAggc+fOdXu+m266ST7wUINun5pm0qRJsnjxYjl16pS8/PLLYoyR9u3by/79+x37Hj16VBYtWuSomS1K9BtU76E1nLlegGrAVcDVQFV/x6NltPf0d4P39Fl5T5+V9/RZee9Cy+fcFmy1gYsv5IIXumhh5j2v/iNlZIjExopUry5y5ozPYyqsfPFL5+jRozJgwAAZMWJEljkqN23aJAEBAQLIfffdl+XYlStXOprqPvzwwzleKyMjQ06cOJFl/enTp2XMmDECSOfOnSU9Pd3r+I8dOyYTJ06UOnXqZGkya39e/fv3F0A2bNjg9Xntli1bJlFRUS5NkgG57rrr5Ewx+lnUAs17mnB6XRY3tr22drf4MzYto72nvxu8p8/Ke/qsvKfPynsXWj57HDTIGNMDKCsi3zvVhu40xgw1xlQRkbl5rVVVhYgx1sBBXbtaU6VMmADXXw8B3rS2VtmpWLEi33//vdttTZo0Yfjw4XzyyScuAwbZNWvWjICAADIyMrwaldcYQ9myZbOsL126NK+//jp33HEHNWvWzDKVR3YqVKjAww8/zMMPP+xxn/vuu4/ExETq1s19K/tLLrmEAwcOsGfPHtasWeOYj/TOO+/MVZxKlUBjgduBV91sE6BrwYajlFJKeZbdKLUTgH5u1v8OTAc04SwuunSBn3+GceNgyBB4/nl48UXI5fQjKneeffZZzp496zahDA0NpVGjRuzdu5eOHTte8LUaN258wedwp3PnzixbtizPxxtjqFWrFrVq1eKqq67Kx8iUKr5E5Hbb214ics55mzEm1A8hKaWUUh5lV41QWkQOZV4pIvuBMr4LSflFv36wdi18/TWcOwd9+sAFJBIqZ9WqVePrr7/2OJXG2LFjmTBhAsFFYMoapZRfLPFynVJKKeU32SWcocaYLDWgxphgIMzN/lkYY3oaY/4xxmwzxnicF8wY084Yk26MGejNeZWPBAZaNZyrVkGlSvDMM/6OqES77bbbGD16tL/DUEoVMsaYqsaYNkCYMaaVMaa1bekMeDWEs5bPSimlCkp2CeePwIfGGEdtpu39+7Zt2TLGBALvAL2ApsAQY0xTD/tNBH7NXejKZ8qWhbFj4ZdfrORTKaVUYdIDeAWoidWP077cD4zL6WAtn5VSShWk7BLOJ4ADQIIxZpUxZhWwEzhk25aTS4BtIrJDRFKAaVjDtmd2L/AD1vyeqrC45x6oUMGao1MppVShISKfAVcAd4pIVxHpYluuFpEcvxBGy2ellFIFyGPCKSJpIvIoUAsYbluiReRREUn14tw1gN1On/fY1jkYY2oA/bFqTVVhUr48jBkD//d/8Pff/o5GKaWUExHJAO7I4+FaPiullCow2Y1SC4CInAXW5+Hcxt3pMn1+A3hERNKNcbe77UTG3I41BDxRUVEsWLAgD+GUPKdOnbqgZxXUujWXlinD0dGj2TR+fL7FVRhd6LMqafR5eU+flfKhucaYB4FvgNP2lSJyNIfj8q18Bi2j80p/N3hPn5X39Fl5T59Vwckx4bwAe7BqR+1qAnsz7dMWmGYrzCKB3saYNBH5P+edRGQyMBmgUaNG0rlzZx+FXLwsWLCAC35W999P5WefpXJkJDRvni9xFUb58qxKEH1e3tNnpXzoVtvr3U7rBMhpYtx8K59By+i80t8N3tNn5T19Vt7TZ1VwfDm7+gqggTGmjjGmFDAY+Nl5BxGpIyK1RaQ28D1wl7vCTPnRmDEQHg5PPQWS+QtwpZRS/mIrQzMvOSWboOWzUkqpApRjwuk03LrzUs/dlCnORCQNuAdrdLvNwLcistEYc6cx5s78CV/5XEQEPPII/PgjvPqqv6NRSillY4wJNsbcZ4z53rbcY5u6LFtaPiullCpI3jSpfRdoDazD6vfR3PY+whhzp4j85ulAEZkFzMq0zu0ABCIy3MuYVUEbNw7WrYOHHoLatWGgTsemlFKFwHtAMFY5DXCjbd1tOR2o5bNSSqmC4k3CuRMYISIbAWxzdT0EPIM1H6fHhFMVEwEB8NlnkJgIN94INWpAhw7+jkoppUq6diLSwunzH8YYHVZcKaVUoeJNH87G9mQTQEQ2Aa1EZIfvwlKFTlgY/PSTlWxedRUsXQrp6f6OSimlSrJ0Y0w9+wdjTF1AfzErpZQqVLyp4fzHGPMe1sTQAIOAf40xIYA383Gq4iIyEmbNgo4draVcOev18svhzjuhQgV/R6iUUiXJQ8B8Y8wOrC4vMcAt/g1JKaWUcuVNwjkcuAsYg1WgLQIexEo2u/gqMFVINWwImzbB3LkQH28tjz0GP/xgrdOkUymlCoSI/G6MaQA0wiqft4hIsp/DUkoppVzkmHCKyFngVduS2al8j0gVfpUrw9Ch1gIwcyZcey306AG//Qbly/s3PqWUKgGMMaFYXwjHYs2/GW+MeV9Ezvk3MqWUUuo8b6ZF6WSMmWuM+dcYs8O+FERwqojo2xe++w5Wr4aePeHECX9HpJRSJcHnQDPgLeBtoCnwhV8jUkoppTLxpkntx8D9wCp0MALlydVXw7ffwvXXQ+/e8McfUKqUv6NSSqnirFGmUWrn6yi1SimlChtvRqlNEpHZInJQRI7YF59Hpoqe/v1hyhRYvBimTctxd6WUUhdkjTHmUvsHY0x7YLEf41FKKaWy8CbhnG+MedkY08EY09q++DwyVTTdcAM0awavvw4i/o5GKaWKs/bAEmPMTmPMTmApcLkxZr0xZp1/Q1NKKaUs3jSpbW97beu0ToCu+R+OKvKMgTFjYORIWLgQOnf2d0RKKVVc9fR3AEoppVROvBmlVqc+UbkzdKg1Vcrrr2vCqZRSPiIiCcaYFkCcbVW8iGgfTqWUUoWKx4TTGDNMRL40xox1t11EXvNdWKpICwuDO++E556Dbdugfn1/R6SUUsWOMWY0MBL40bbqS2PMZBF5y49hKaWUUi6y68NZxvZa1s0S7uO4VFF3110QFASTJvk7EqWUKq5GAO1F5EkReRK4FCsBVUoppQoNjzWcIvKB7e08EXEZ9c4Y08mnUamir1o1GDIEPv0UnnkGKlTwd0RKKVXcGFynK0u3rVNKKaUKDW9GqXXXNEeb66icjRkDp0/DRx/5OxKllCqOPgGWGWPGG2PGA39hzZ2tlFJKFRrZ9eHsAHQEojL14ywHBPo6MFUMtGoFl19u9eWsWxeuvdbfESmlVLFgjAkAlgELgVisms1bRGSNXwNTSimlMsmuhrMUVl/NIFz7b54ABvo+NFUsfPwx1KsHAwbALbfAiRP+jkgppYo8EckAXhWR1SLypohM0mRTKaVUYZRdH86FwEJjzBQRSQDHN6rhIqJZg/JOvXqwZAk8/TS88AIsWACTJ0O3bv6OTCmlirrfjDEDgB9FRPwdjFJKKeWON304XzDGlDPGlAE2Af8YYx7ycVyqOClVCp59FuLjITAQuneHK66Av/7yd2RKKVWUjQW+A5KNMSeMMSeNMfqFsFJKqULFm4Szqa1G8xpgFhAN3OjLoFQx1bEjbNhgTZWyYQN06ABXXw1Hjvg7MqWUKnJEpKyIBIhIKREpZ/tczt9xKaWUUs68STiDjTHBWAnnTyKSCmjTHZU3oaFw332wfbs1mNDMmfDmm/6OSimligxjTANjzE/GmA3GmK+NMTX8HZNSSinliTcJ5wfATqAM8KcxJgZr4CCl8i48HMaNg3btYO5cf0ejlFJFySfATGAAsAadqkwppVQhlmPCaRv9roaI9BZLAtClAGJTJUH37rBsGRw/7u9IlFKqqCgrIh+KyD8i8jJQ298BKaWUUp7kmHAaY6oYYz42xsy2fW4K3OzzyFTJ0L07ZGTAH3/4OxKllCoqQo0xrYwxrY0xrYGwTJ+VUkqpQsPjtChOpgCfAo/bPv8LfAN87KOYVEnSvj2ULQu//QbXXuvvaJRSqijYB7zm9Hm/02cBuhZ4REoppZQHHhNOY0yQiKQBkSLyrTHmMQARSTPGpBdYhKp4Cw6Grl3h119BBIzxd0RKKVWoiYh2a1FKKVVkZNekdrnt9bQxJgLbyLTGmEuBJF8HpkqQ7t1h505r5FqllFJKKaVUsZFdk1p7VdNY4GegnjFmMRAFDPR1YKoE6d7dev3tN6hf//z69HQ4e9Ya0VYppZRSSilV5GRXwxlljBkLdAamAy8Bs4EPgSt9H5oqMerVgzp1rITTTgSGDIFmzSAtzX+xKaWUUkoppfIsuxrOQCCc8zWddqV9F44qkYyxajm//hpSU61+nVOmwHffWdvnz4du3fwaolJKFTYeRqRNAhJsYzAopZRSfpddwrlPRJ4usEhUyda9O3zwgTUnZ/XqcN99cNll8Pff8NVXmnAqpVRW7wKtgXVYXw43t72PMMbcKSK/ZXewUkopVRCya1Krw4WqgtO1KwQEwKxZcOONEBgIX3wBAwbAjz9afTmVUko52wm0EpG2ItIGaAVswOr28pI/A1NKKaXssks4ryiwKJSqUMGak/OVV2DJEnj3XYiOhhtugJMn4Zdf/B2hUkoVNo1FZKP9g4hswkpAd/gxJqWUUsqFx4RTRI4WZCBK0b271Ydz8GAr0QTo3BmqVrX6dyqllHL2jzHmPWPM5bblXeBfY0wIkOrv4JRSSinIvg+nUgVr+HDYvduq5bQLDLQS0HffhePHrZpQpZRSAMOBu4AxWN1gFgEPYiWbXfwWlVJKKeUkuya1ShWs2rXh44+hYkXX9TfcACkp8MMPfglLKaUKIxE5KyKvikh/EblGRF4RkTMikiEip/wdn1JKKQWacKqioG1baNBAm9UqpZQTY0wnY8xcY8y/xpgd9sXfcSmllFLONOFUhZ8xVi3n/Pmwd6+/o1FKqcLiY+A1IBZo57QopZRShYYmnKpoGDIERLSWUymlzksSkdkiclBEjtgXfwellFJKOdOEUxUNjRpBXBw8+ST8/ru/o1FKqcJgvjHmZWNMB2NMa/vi76CUUkopZzpKrSo6vv8errwS+vaFn36yplFRSqmSq73tta3TOgG6+iEWpZRSyi1NOFXRUbky/PGHlXRedRVMnw69evk7KqWU8gsR0alPlFJKFXqacKqiJTLSSjq7dYNrroHx4+Guu6B8eX9HppRSBcIYM0xEvjTGjHW3XUReK+iYlFJKKU+0D6cqeipVgnnzrCa148ZBTAw88QQcOuTvyJRSqiCUsb2WdbOE+ysopZRSyh1NOFXRVLEizJgBK1daTWyffx7q1IEvvvB3ZEop5VMi8oHt7TwRmeC8ADqqmlJKqUJFE05VtLVpYw0mtHGj9f6mm6wmtsnJ/o5MKaV87S0v1ymllFJ+o304VfHQpIk1Xcpjj8Err8Dq1fDdd1Crlr8jU0qpfGWM6QB0BKIy9eMsBwT6JyqllFLKPa3hVMVHUBC8/LJV47lpkzVvZ2qqv6NSSqn8Vgqrr2YQrv03TwAD/RiXUkoplYXWcKriZ8AACAyE/v1hzhzo18/fESmlVL4RkYXAQmPMFBFJADDGBADhInLCv9EppZRSrrSGUxVPfftClSrwySf+jkQppXzlBWNMOWNMGWAT8I8x5iF/B6WUUko504RTFU9BQXDjjTBzJhw86O9olFLKF5raajSvAWYB0cCNfo1IKaWUykQTTlV83XILpKXBl1/6OxKllPKFYGNMMFbC+ZOIpALi35CUUkopV5pwquKraVNo395qViv6N5hSqtj5ANgJlAH+NMbEYA0cpJRSShUamnCq4u3WW605Oleu9HckSimVr0TkTRGpISK9xZIAdPF3XEoppZQzTThV8TZoEISF6eBBSqlixxhTxRjzsTFmtu1zU+BmP4ellFJKufBpwmmM6WmM+ccYs80Y86ib7UONMf/f3p2HW1mXCx//3rA3kyDEZMxoIoUpTqH1muLRUuskVmKaqS8eRBM9aJpDNumrhyyvgq4cM/RYiql1jKujYQ68apY4hL6iUQYewInEgUEFhN/7x2/t9gY2sIC91rOH7+e6nmut9axnrXXvG1g39/49z+/3TGl7NCJGVjIetUHdu+dlUqZPh3ffLe81M2fCaafBunWVjU2Sts9NwEygf+nxX4Gzy3mh9VmSVC0Vazgjoj1wFXAkMAI4vvTb14YWAAenlPYE/g9wfaXiURs2bhy8/Tb8/Od5EqEt+eY34frr4cYbKx+bJG2liKhbQ7t3Sul2YB1ASul9YG0Zr7c+S5KqppIjnKOAF1JK81NKq4HbgDEND0gpPZpSerP08E/AwArGo7Zq9GjYddc8atmlCwwfDmPGwPPPb3zsnDn5es8uXeCii+Ctt6ocrCRt0ezS7cqI6EVpZtqIOAB4u4zXW58lSVVTs+VDttkAYFGDx4uB/Tdz/L8B9zT2RERMACYA9OnTh1mzZjVRiK3bihUrzFVJh8mT6Tl7Nl0WL6bz4sV84P77WXH88cyZMgWoz9WwqVPpV1vLM5deysivf53Fp57K3ydOLDb4Zsi/W+UzV6qAKN1+DZgBfCgi/gD0AY4p4/VNVp/BGr2t/G4on7kqn7kqn7mqnko2nNHIvkbXpoiIQ8gF7cDGnk8pXU/pdJ7hw4en0aNHN1GIrdusWbMwVw0c0+D/YT/+MT0mTWJ0SnDIITlXo0bBgw/C2LHsde65MG8eg268kUGXXgof+UhxcTdD/t0qn7lSBfSJiK+V7v8XcDe55q4CDgOe2cLrm6w+gzV6W/ndUD5zVT5zVT5zVT2VPKV2MTCoweOBwMsbHhQRewI3AGNSSksrGI9Ub8IE6N8fvvvd+jU6f/WrfK3nqafmx5dfDjvsAGef7TqekpqT9kBXoBt5Dc6a0r4upX1bYn2WJFVNJUc4HweGRcTOwEvAccCXGx4QEYOBXwMnppT+WsFYpPV16pSv0TzrrDyq2a4d/PSn+VrPgw/Ox/TpkxvSc86Bu+6Cz3++yIglqc4rKaVLt+P11mdJUtVUbISzNFvemeQp258Hbk8pzY2I0yPi9NJh3wZ6AVdHxJyIeKJS8UgbGT8eBgyA73yHzgsXwsMP533R4GyziRNh993h2GPhwgth5cri4pWkrLFTYstmfZYkVVMlRzhJKd1Nvrak4b5rG9wfD4yvZAzSJtWNcp55Jh9+802oqYGTN1gzvbY2j4BecAFccQXceitMnQpHH71+YypJ1XPo9r6B9VmSVC2VvIZTav7Gj4eBA+k+dy587nPwwQ9ufEyfPjBtWh4B7dEDvvAFuO66qocqSQAppTeKjkGSpHLZcKpt69gRLr44358wYfPHHnggPPkk7LuvDackSZJUBhtO6bTTePLaa+GII7Z8bG0tnHQSzJkDzz1X8dAkSZKklsyGU4pg+fDh5R9/7LF5VttbbqlcTJIkSVIrYMMpba0PfhAOOyxPIOT6nJIkSdIm2XBK2+KEE+DFF+GPfyw6EkmSJKnZsuGUtsXnPw+dO3tarSRJkrQZNpzStujWDY46Cm6/HdasKToaSZIkqVmy4ZS21QknwOuvw+9/X3QkkiRJUrNkwyltq8MPh549Pa1WkiRJ2gQbTmlbdegAY8fCXXfBihVFRyNJkiQ1Ozac0vYYNw7eeQfGj4d164qORpIkSWpWbDil7bH//vD978MvfwkXXVR0NJIkSVKzUlN0AFKLd955eU3O738fhgyBM84oOiJJkiSpWbDhlLZXBEydCosWwVlnwaBB8LnPFR2VJEmSVDhPqZWaQk0NTJ8O++wDX/gCTJoES5cWHZUkSZJUKBtOqanssAP87ndwyinwk5/ArrvCj34Eq1YVHZkkSZJUCBtOqSn16gXXXQdPPw2jRsHXvpYb0aFD4aCD4KST4Nlni45SkiRJqgqv4ZQq4aMfhZkz4b774MEHYeFC+J//gd/8Bh59FObMga5di45SkiRJqigbTqmSDjssb3UeeghGj4bzz4erry4sLEmSJKkaPKVWqqaDDsqn2V5zTR4BlSRJkloxG06p2i67DEaMyJMLvflm0dFIkiRJFWPDKVVbp05w882wZAmceWbR0UiSJEkVY8MpFWHffeFb34Jbb4XPfAaeeGL95597Ds49Ny+vIkmSJLVQThokFeUb38ijnVdcAR/7GBx1FBx5JNxyCzzySD6mXTv45Cdh5MiNX79gAQwZko+RJEmSmiH/pyoVpaYmz1a7YEG+rvOhh+CrX4XXXoMf/ADmzYOePfNptymt/9obboBddoFPfAIee6yY+CVJkqQtsOGUirbjjnDxxfDii/DUU7nRPO882G03mDw5j3beemv98U8+mZvQ/fbL63secACceCK89FJhP4IkSZLUGBtOqbno3h323hsi6vedcko+3fa882DZMnjjDTjmGOjbF+65B/7613xq7h13wB575NFRSZIkqZmw4ZSas3bt8sRBr74Kl1xSP5J5553Quzd07QqXXw6PP54b0u99r+iIJUmSpH+y4ZSau1Gj8kjnD38Id98NU6fmfQ3tsQecfDJccw0sXlxMnJIkSdIGbDillmDyZNhpJxg3Dk4/vfFjvvUtWLcuj3hKkiRJzYANp9QS9O2bJxWaNm39azwbGjoUTj01z2C7YEE1o5MkSZIaZcMptRSdOm35mIsvzsutXHpp5eORJEmStsCGU2pN+veHiRPh5pvz8iqSJElSgWw4pdbmggugc2c47TQnEJIkSVKhbDil1qZPH5gyBf70Jxg+HC67DN59t+ioJEmS1AbZcEqt0fjx8PzzcOSRefbaESPg29+G6dNhzhwbUEmSJFWFDafUWu28M9x5JzzwQB71vPxy+PKXYe+9oVs3mDABXn656CglSZLUitlwSq3dIYfA7NmwciU88wzcfnu+vvOmm2DXXeGb34Rly5r+c+fPh8MOg3POgfffb/r3lyRJUrNnwym1FZ06wR57wNixcNVV8Je/wNFH55HPIUPyGp733w9r127/Z915Zx5JffTRfD3pUUfB8uXb/76SJElqUWw4pbZql13g1lvhiSfgs5+F227LI5IDBsCZZ8KsWes3nytW5EZy0iR6PPVU4+/53nt5WZaxY+HDH4bnnoPrroN774VPfjLPmpsSLFgAP/85XHmlo5+SJEmtWE3RAUgq2L77wi9+kScSuvvu3HhOm5ZHQXfaKY+CvvoqzJyZG8p27dhr3Tq45x74j/+Aj30sT1B04415/c/XXoNzz83PdeiQrxUdOhSOOQb22Sfve+ml+s/v0gXOOKOon16SJEkV5AinpKxzZ/jiF+GOO2DJEvjlL+Ggg/JI5JNP5sZx1ixYtowXJk7Ms92OGpVHMkeMgB/9CD7+8Xxa7pVX5sayzqc/DX/4A+y5Zx7pvOoqePppOPTQfA3p0qVF/dSSJEmqIEc4JW2sa1c49ti8vf8+tG8PEf98evExx7Dr5Mm5yXzggXz951e+kkdEN2WPPeC++9bfN3UqjByZl265+uoK/TCSJEkqiiOckjavpma9ZvOfunXLa3vOmpVPod1cs7kpu++erxe97ro8YipJkqRWxYZTUrG++13o2RPOOitPKCRJkqRWw4ZTUrF69IDJk+GRR2D69KKjkSRJUhOy4ZRUvFNOgf32gxNPzJMK/eAHMG9e0VFJkiRpOzlpkKTitWsHM2bAtdfm2/PPz1vXrvna0L5981ZTA+vW5S0CunfPI6Q9ekDv3jBwIAweDIMG5ceNXXsqSZKkqrHhlNQ89OsHl1ySt4UL4be/hb/9La/r+dprMH9+bjTbtcvbunWwbBm89Ra8/fbG79e+PXzgA9CrV95GjcrLsxx8cF77E2D16vxZ69bBsGE2qJIkSU3MhlNS8zN4MJxxRvnHr12b1/JctChvCxfmtUSXLoU33oBXXoFrroEpU/L6oCNH5ucXLcrNJuTR0cMPhyOOyOuP9u1bkR9NkiSpLbHhlNTytW9ff9rtvvs2fsy778LDD8PMmfDnP8Nuu8Euu+Rt1Sq491644w742c/y8f37w1575ea0Xz/Ycce8demSj3/nHVi5Mn923fv0759HX9esyc3uW2/R7r33qpYGSZKk5saGU1Lb0LlzPqX2059u/PnTTsuN4mOPwezZeV3QOXNyg7p2bXmf0akT1NbC8uX/3HVgTQ3sv38+lXf//fNpvHUjr2vX5lN5hw/PDXDdqb6SJEmthA2nJNWprYUDD8xbnTVr8rWib7+db1euzM1rly55W7MmX1/697/nbfXqPGFRr16w444s/u//ZvCCBXDFFVtuXHv3rp8EqUeP+ga2Q4c8YVLDa0zbt88jrt27r39bt3XrVr917QodO+bRV0mSpCqqaMMZEUcAU4H2wA0ppe9t8HyUnv8M8A7wv1NKT1UyJknaKrW19RMPbcqHPgSf+lSjT80fOJDBo0fnUc+5c2GHHfJ79ewJKeWJkebNy9vLL+dJkOq2pUtzQ7t6db5t2HCuWZPfc9my+utQy/lZOnbMjWzd1rlz3texY25sO3bMzWxEblAj1v/ctWvrP3fZshxbbW1uiGtrc9M8ZEjeBg/OjXNd09u1a30DXVubP99rZQthfZYkVUvFGs6IaA9cBXwKWAw8HhEzUkrPNTjsSGBYadsfuKZ0K0mtS7ducMABG+/fc8+8bauU8qhrXQNYNxq7fDmsWFF/+957eVu1qv5+w23Vqtw8rliRm8qU8rZhM9uuXR5B7dcvnwrcoQO8/359Y7xkCdx3X26eU9p87Lvt5nqrBbA+S5KqqZIjnKOAF1JK8wEi4jZgDNCwoI0Bbk4pJeBPEdEjIvqllF6pYFyS1HpE1I8e9u9fdDT1Vq/OTeeyZbnpXb48N8Zr1tQ3p127Fh1lW2V9liRVTSUbzgHAogaPF7Pxb0cbO2YAYEGTpJasQwcYOrToKNQ467MkqWoq2XA2toL6hudXlXMMETEBmFB6uCoint3O2NqK3sDrRQfRQpirrWO+ymeuyje86ADaiCarz2CN3g5+N5TPXJXPXJXPXJVvu+pzJRvOxcCgBo8HAi9vwzGklK4HrgeIiCdSSvs1baitk7kqn7naOuarfOaqfBHxRNExtBFNVp/BGr2tzFX5zFX5zFX5zFX5trc+V3KO/MeBYRGxc0R0AI4DZmxwzAzgpMgOAN72+hBJkirK+ixJqpqKjXCmlN6PiDOBmeRp16ellOZGxOml568F7iZPuf4Cedr1cZWKR5IkWZ8lSdVV0XU4U0p3k4tWw33XNrifgIlb+bbXN0FobYW5Kp+52jrmq3zmqnzmqkoqVJ/BP8OtYa7KZ67KZ67KZ67Kt125irSlddIkSZIkSdoGlbyGU5IkSZLUhrWohjMijoiIeRHxQkRcWHQ8zUlEDIqIByPi+YiYGxGTSvt7RsTvI+JvpdsPFB1rcxER7SPizxHx29Jjc9WI0oLvd0bEX0p/vz5urhoXEeeU/v09GxHTI6KTucoiYlpELGm4ZMbmchMRF5W+6+dFxOHFRK1yWZ83zfq89azP5bE+l8/6vHmVrtEtpuGMiPbAVcCRwAjg+IgYUWxUzcr7wLkppY8ABwATS/m5ELg/pTQMuL/0WNkk4PkGj81V46YCv0spfRgYSc6ZudpARAwA/h3YL6X0UfJkLMdhrurcBByxwb5Gc1P67joO2L30mqtLNUDNkPV5i6zPW8/6XB7rcxmsz2W5iQrW6BbTcAKjgBdSSvNTSquB24AxBcfUbKSUXkkpPVW6v5z8pTOAnKP/LB32n8DRhQTYzETEQOCzwA0NdpurDUTEjsBBwM8AUkqrU0pvYa42pQboHBE1QBfyuoXmCkgpPQS8scHuTeVmDHBbSmlVSmkBeabUUdWIU9vE+rwZ1uetY30uj/V5q1mfN6PSNbolNZwDgEUNHi8u7dMGImIosDfwGLBT3dpppdu+BYbWnEwBzgfWNdhnrja2C/AP4MbS6U03RMQOmKuNpJReAq4EFgKvkNctvBdztTmbyo3f9y2Lf15lsj6XZQrW53JYn8tkfd5mTVajW1LDGY3sc4rdDUREV+BXwNkppWVFx9McRcS/AktSSk8WHUsLUAPsA1yTUtobWEnbPuVkk0rXNowBdgb6AztExFeKjarF8vu+ZfHPqwzW5y2zPm8V63OZrM9Nbqu/81tSw7kYGNTg8UDycLhKIqKWXMxuSSn9urT7tYjoV3q+H7CkqPiakf8FHBURL5JP/fqXiPgF5qoxi4HFKaXHSo/vJBc4c7Wxw4AFKaV/pJTWAL8GPoG52pxN5cbv+5bFP68tsD6XzfpcPutz+azP26bJanRLajgfB4ZFxM4R0YF8seqMgmNqNiIiyOfxP59S+mGDp2YAJ5funwz8ptqxNTcppYtSSgNTSkPJf48eSCl9BXO1kZTSq8CiiBhe2nUo8BzmqjELgQMiokvp3+Oh5Gu1zNWmbSo3M4DjIqJjROwMDANmFxCfymN93gzrc/msz+WzPm8V6/O2abIaHSm1nLNeIuIz5HP72wPTUkqXFxtR8xERBwIPA/+P+usevkG+TuR2YDD5H9zYlNKGFwW3WRExGjgvpfSvEdELc7WRiNiLPHlDB2A+MI78yypztYGIuAT4EnlWyj8D44GumCsiYjowGugNvAZ8B7iLTeQmIi4GTiHn8uyU0j3Vj1rlsj5vmvV521ift8z6XD7r8+ZVuka3qIZTkiRJktRytKRTaiVJkiRJLYgNpyRJkiSpImw4JUmSJEkVYcMpSZIkSaoIG05JkiRJUkXYcEpVEBFrI2JOg+3CJnzvoRHxbFO9nyRJbYX1Waq8mqIDkNqId1NKexUdhCRJWo/1WaowRzilAkXEixFxRUTMLm27lvYPiYj7I+KZ0u3g0v6dIuK/IuLp0vaJ0lu1j4ifRsTciLg3IjoX9kNJktTCWZ+lpmPDKVVH5w1O2flSg+eWpZRGAT8BppT2/QS4OaW0J3AL8OPS/h8D/zelNBLYB5hb2j8MuCqltDvwFvDFiv40kiS1DtZnqcIipVR0DFKrFxErUkpdG9n/IvAvKaX5EVELvJpS6hURrwP9UkprSvtfSSn1joh/AANTSqsavMdQ4PcppWGlxxcAtSmly6rwo0mS1GJZn6XKc4RTKl7axP1NHdOYVQ3ur8XrsyVJ2l7WZ6kJ2HBKxftSg9s/lu4/ChxXun8C8Ejp/v3AVwEion1E7FitICVJamOsz1IT8LcsUnV0jog5DR7/LqVUN/V6x4h4jPwLoONL+/4dmBYRXwf+AYwr7Z8EXB8R/0b+TelXgVcqHbwkSa2U9VmqMK/hlApUukZkv5TS60XHIkmSMuuz1HQ8pVaSJEmSVBGOcEqSJEmSKsIRTkmSJElSRdhwSpIkSZIqwoZTkiRJklQRNpySJEmSpIqw4ZQkSZIkVYQNpyRJkiSpIv4/v7hooGhNkAIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " Result when 6 initial channels are used\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " Result when 7 initial channels are used\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def CNN_model(Num_channel):                                        # CNN design function\n",
    "    model = tf.keras.Sequential()                       # define neural network model sequentially\n",
    "    # Convolution Feature Map 1: (12x12x1) --> (12x12x6)\n",
    "    model.add(layers.Conv2D(int(Num_channel), kernel_size=(3,3),strides=1,padding='same',input_shape=[ny,nx,1])) # -2 feature map extent given filter and no padding\n",
    "    model.add(layers.ReLU())\n",
    "    # Max Pooling Feature Map 1: (12x12x6) --> (6x6x6)    \n",
    "    model.add(layers.MaxPooling2D((2, 2)))              # 1/2 extent\n",
    "    # Convolution Feature Map 2: (6x6x6) --> (6x6x12)\n",
    "    model.add(layers.Conv2D(int(Num_channel*2), kernel_size=(3,3),strides=1,padding='same',input_shape=[ny,nx,1])) # -2 feature map extent given filter and no padding\n",
    "    model.add(layers.ReLU())\n",
    "    # Max Pooling Feature Map 2: (6x6x12) --> (3x3x12)    \n",
    "    model.add(layers.MaxPooling2D((2, 2)))              # 1/2 extent\n",
    "    # Convolution Feature Map 3: (3x3x12) --> (3x3x24)\n",
    "    model.add(layers.Conv2D(int(Num_channel*4), kernel_size=(3,3),strides=1,padding='same',input_shape=[ny,nx,1])) # -2 feature map extent given filter and no padding\n",
    "    model.add(layers.ReLU())\n",
    "    # Max Pooling Feature Map 3: (3x3x24) --> (1x1x12)    \n",
    "    model.add(layers.MaxPooling2D((3, 3)))              # 1/2 extent\n",
    "    model.add(layers.Flatten())\n",
    "    # Output layer: (12) --> 2 (i.e., each node corresponds to the probability to be each class)\n",
    "    model.add(layers.Dense(2, activation = 'softmax'))  # softmax activation function for classfier probabilities\n",
    "    # Compile the Neural Network - define Loss and optimizer to tune the associated weights\n",
    "    loss = tf.keras.losses.SparseCategoricalCrossentropy()\n",
    "    metrics = tf.keras.metrics.SparseCategoricalAccuracy()\n",
    "    model.compile(loss=loss, metrics=[metrics], optimizer='adam')\n",
    "    return model\n",
    "\n",
    "nepoch = 100; batch_size = 5                             # training parameters\n",
    "\n",
    "\n",
    "for i in [4,5,6,7]:\n",
    "    tf.random.set_seed(seed)\n",
    "    model = CNN_model(i)\n",
    "    history = model.fit(X_train[:,:,:,0].reshape([ntrain,ny,nx,-1]), y_train,\n",
    "                          batch_size=batch_size, epochs=nepoch, verbose=0,\n",
    "                          validation_data=(X_test[:,:,:,0].reshape([ntest,ny,nx,-1]), y_test))\n",
    "    print(f'\\n\\n Result when {i} initial channels are used')\n",
    "    model_performance(history,nepoch)                       # plot loss and accuracy over training Epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the results we can conclude that the models starting with 4 and 6 channels performed the best. We will go forth with the model starting with 6 channels\n",
    "* Starting with 6 channels allows for a diversity of filters that aid in identification of artifacts\n",
    "* Using (3,3) kernel filters reduces the amount of information coming from padding\n",
    " * When using larger kernel filters, a large amount of information would come from \"padding\" (which are simply zeros)\n",
    "* We will use feature maps that have (12x12), (6x6), and (3x3) sizes \n",
    "* Max pooling is used after each convolution to help find the feature map containing the most prominent features of the previous feature map\n",
    "* Optimize using sparse categorical crossentropy to compute the crossentropy loss between the labels and predictions\n",
    "* ReLu and Softmax are used to restrict values between [0,1], make probability predictions, and prevent the “vanishing gradient” problem caused by other activation functions\n",
    "* Epochs are reduced from 100 to prevent over training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CNN_model():                                        # CNN design function\n",
    "    model = tf.keras.Sequential()                       # define neural network model sequentially\n",
    "    # Convolution Feature Map 1: (12x12x1) --> (12x12x6)\n",
    "    model.add(layers.Conv2D(6, kernel_size=(3,3),strides=1,padding='same',input_shape=[ny,nx,1])) # -2 feature map extent given filter and no padding\n",
    "    model.add(layers.ReLU())\n",
    "    # Max Pooling Feature Map 1: (12x12x6) --> (6x6x6)    \n",
    "    model.add(layers.MaxPooling2D((2, 2)))              # 1/2 extent\n",
    "    # Convolution Feature Map 2: (6x6x6) --> (6x6x12)\n",
    "    model.add(layers.Conv2D(12, kernel_size=(3,3),strides=1,padding='same',input_shape=[ny,nx,1])) # -2 feature map extent given filter and no padding\n",
    "    model.add(layers.ReLU())\n",
    "    # Max Pooling Feature Map 2: (6x6x12) --> (3x3x12)    \n",
    "    model.add(layers.MaxPooling2D((2, 2)))              # 1/2 extent\n",
    "    # Convolution Feature Map 3: (3x3x12) --> (3x3x24)\n",
    "    model.add(layers.Conv2D(24, kernel_size=(3,3),strides=1,padding='same',input_shape=[ny,nx,1])) # -2 feature map extent given filter and no padding\n",
    "    model.add(layers.ReLU())\n",
    "    # Max Pooling Feature Map 3: (3x3x24) --> (1x1x12)    \n",
    "    model.add(layers.MaxPooling2D((3, 3)))              # 1/2 extent\n",
    "    model.add(layers.Flatten()) \n",
    "    # Output layer: (12) --> 2 (i.e., each node corresponds to the probability to be each class)\n",
    "    model.add(layers.Dense(2, activation = 'softmax'))  # softmax activation function for classfier probabilities\n",
    "    # Compile the Neural Network - define Loss and optimizer to tune the associated weights\n",
    "    loss = tf.keras.losses.SparseCategoricalCrossentropy()\n",
    "    metrics = tf.keras.metrics.SparseCategoricalAccuracy()\n",
    "    model.compile(loss=loss, metrics=[metrics], optimizer='adam')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instantiate, Train the Convolutional Neural Network and Visualize the Model Performance in Training and Testing\n",
    "\n",
    "This includes the following steps:\n",
    "\n",
    "1. instantiate the CNN specified above \n",
    "\n",
    "2. train it with the 80 images in the training set\n",
    "\n",
    "3. visualize the training and testing accuracy over the Epochs of training\n",
    "\n",
    "4. write out the summary of the convolutional neural network\n",
    "\n",
    "##### Below is the actual CNN model that we just created. If you go back and adjust the model, that change will be represented below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=1134x356 at 0x1C96E048C48>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = CNN_model() \n",
    "visualkeras.layered_view(model, legend=True, scale_xy=20, scale_z=5, spacing=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_15 (Conv2D)           (None, 12, 12, 6)         60        \n",
      "_________________________________________________________________\n",
      "re_lu_15 (ReLU)              (None, 12, 12, 6)         0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_15 (MaxPooling (None, 6, 6, 6)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_16 (Conv2D)           (None, 6, 6, 12)          660       \n",
      "_________________________________________________________________\n",
      "re_lu_16 (ReLU)              (None, 6, 6, 12)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_16 (MaxPooling (None, 3, 3, 12)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_17 (Conv2D)           (None, 3, 3, 24)          2616      \n",
      "_________________________________________________________________\n",
      "re_lu_17 (ReLU)              (None, 3, 3, 24)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_17 (MaxPooling (None, 1, 1, 24)          0         \n",
      "_________________________________________________________________\n",
      "flatten_5 (Flatten)          (None, 24)                0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 2)                 50        \n",
      "=================================================================\n",
      "Total params: 3,386\n",
      "Trainable params: 3,386\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "nepoch = 94; batch_size = 5                             # training parameters\n",
    "\n",
    "tf.random.set_seed(seed) \n",
    "model = CNN_model() \n",
    "history = model.fit(X_train[:,:,:,0].reshape([ntrain,ny,nx,-1]), y_train, \n",
    "                      batch_size=batch_size, epochs=nepoch, verbose=0, \n",
    "                      validation_data=(X_test[:,:,:,0].reshape([ntest,ny,nx,-1]), y_test))\n",
    "    \n",
    "model_performance(history,nepoch)                       # plot loss and accuracy over training Epochs\n",
    "model.summary()                                         # write out the model summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predict with the Trained Convolutional Neural Network\n",
    "\n",
    "Now we load the trained deeper CNN classifier and visualize its prediction performance\n",
    "\n",
    "* we predict over the training and testing image datasets\n",
    "\n",
    "* we predict with the category assigned the maximum probability by our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 451
    },
    "executionInfo": {
     "elapsed": 693,
     "status": "ok",
     "timestamp": 1621130835277,
     "user": {
      "displayName": "HONGGEUN JO",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjjMrjKxVvTiBoV4qCmbZQiE5mDJaodEzKxwowf=s64",
      "userId": "13788572619033134064"
     },
     "user_tz": 300
    },
    "id": "cmo5m_mg1Foe",
    "outputId": "a5ecf913-32d4-4dfc-ad22-b5f93e2da47a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Train   Test\n",
      "precision: 1.0000, 0.9500\n",
      "recall:    1.0000, 0.9500\n",
      "f1 score:  1.0000, 0.9486\n"
     ]
    }
   ],
   "source": [
    "model_cross_validation(model,X_train,X_test,y_train,y_test) # predict and cross validate CNN classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TCdW3Dpz1Foe"
   },
   "source": [
    "### Interogate the Trained Convolutional Neural Network\n",
    "\n",
    "#### Visualize the Convolution Filters\n",
    "\n",
    "We can access and visualize the convolution weights. The follow code:\n",
    "\n",
    "* loops over the layers and finds the convolution layers\n",
    "\n",
    "* loops over the channels and plots all of the filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 408
    },
    "executionInfo": {
     "elapsed": 1382,
     "status": "ok",
     "timestamp": 1621130837142,
     "user": {
      "displayName": "HONGGEUN JO",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjjMrjKxVvTiBoV4qCmbZQiE5mDJaodEzKxwowf=s64",
      "userId": "13788572619033134064"
     },
     "user_tz": 300
    },
    "id": "CpD7lzFR1Foe",
    "outputId": "a81f62b1-6595-4257-e0b4-5f364cc02e21"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convolution Layer Name: conv2d_15, Shape: 3 x 3, Number of Channels: 6.\n"
     ]
    },
    {
     "data": {
      "image/png": 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DWgSmi9enQAyFtljrY/tAMm7y2YFr+WvM7Jqe3ze6+8ae3/uVOuhAHK+V9M0Fu36f4O47zOxASVeY2fXufmXl2YE2oUUghvFbpENgUoZ3KNEiMB28PgViKLhFFk8Rhw98t2LXiIMBb5e0vuf3dZJ2DLjuKVqw67e77+j+c6eZXarO7uRsEFEuWgRiGK9FOgQmaXCHEi0C08PrUyCGQlvkY/uIwSWfnel7qmCrpCPN7AgzW6pOaJsXXsnMVkl6qaS/7zlvhZnts/tnSa+Q9P0J/EVAM9EiEMP4LdIhMClDOqRFYIp4fQrEUHCL7HmKGNzkc+Ot5bv7rJmdIelySTOSLnT368zsbd3LL+he9dclfdXdH+q5+UGSLjUzqdPD5939K2P+FUDz0SIQw5gt0iEwQWwTgRhoEYih4BZZPEUI3n0HY/zb+xZJWxacd8GC3zdJ2rTgvBslHTP2AwMtQ4tADItpkQ6ByWCbCMRAi0AMJbfI4imCMGm+0je0AUiKFoEYaBHIjw6BGGgRiKHcFlk8RQwu+dz472AAmBBaBGKgRSA/OgRioEUghoJbZPEUQZjmxzx2BoBJokUgBloE8qNDIAZaBGIot0UWTxHDIo+dAWBCaBGIgRaB/OgQiIEWgRgKbpHFU4TgbsXu/g1EQotADLQI5EeHQAy0CMRQcossniIML/TAw0A0tAjEQItAfnQIxECLQAyltsjiKWIo+B0MIBRaBGKgRSA/OgRioEUghoJbZPEUIbhU7IGHgUhoEYiBFoH86BCIgRaBGEpukcVTxOCm+ULfwQBCoUUgBloE8qNDIAZaBGIouMXki6fLnrFST7/0hNQPMxFfOuHg3CPU8ro3fjn3CJNV6LEzpmV2+yO6909vyD1GJZdcc3zuEWo5730bc49Q2SffWeFKtJjU7TvX6IPn/lbuMSp571s/l3uEWm58cHnuESp5dL7iO/a0mJZJtud87ikqeeGr/jn3CLX857/+/dwjVHLzvRePvhIdJjUn0wOzzdiL6bAVj+ceoZZ3nNacbfjH3lfhSrSY1LYfrdJrf+M1ucdopa889IncI0xWoS2y5yli8HJ3/wZCoUUgBloE8qNDIAZaBGIouEUWTxGCq9zdv4FIaBGIgRaB/OgQiIEWgRhKbpHFU8Tg0nzVjzE2jJkdIukESS9293flngcYihaBGFraIh2iUVraoUSLaBhaBGIouEUWTxFCW97BMLMlkn5Rnehe1P3nnpK+KemqjKMBldAiEEMbWqRDNF0bOpRoEc1Hi0AMJbfI4ilicMnbceDhuyXdJekfuqc/c/fteUcCaqBFIIZ2tEiHaLZ2dCjRIpqOFoEYCm6RxVOE0ZLdv8/Vz9+5WCVplZldJek6d/eskwEV0SIQQwtapEM0Xgs6lGgRLUCLQAyltsjiKUJwt1Z8a5u7nyVJZmaSjlYnyHdLOtrMdrn7hpzzAaPQIhBDG1qkQzRdGzqUaBHNR4tADCW3yOIpwpibb/6xM3qslrRO0lpJB0t6VNItOQcCqqJFIIYWtbhadIiGalGHEi2iwWgRiKHUFlk8RQxurTh2hpl9Rp0DD98r6VvqHHD4I+5+X865gMpoEYihBS3SIRqvBR1KtIgWoEUghoJbZPEUIbhac+yM49V59+JmSfdJul/SwzkHAuqgRSCGlrRIh2i0lnQo0SIajhaBGEpukcVTxODWit2/3f0oM1sj6Y2STpT0G5KeZWbXS7rK3d+dcz5gJFoEYmhBi3SIxmtBhxItogVoEYih4BZZPEUILrXiwMNmdoykSyTdKek5kr4vaYM6f+IJGUcDKqFFIIY2tEiHaLo2dCjRIpqPFoEYSm6RxVPE4NJcO3b//qikN7v71Wb2HUknS/o7dz9R0taskwFV0CIQQztapEM0Wzs6lGgRTUeLQAwFt8jiKUJwmdybf+BhSavc/eruz+bud5nZiqwTATXQIhBDS1qkQzRaSzqUaBENR4tADCW3yOIpwmjJOxgzZraHu89KWmJmvyVpV+6hgDpoEYihBS3SIRqvBR1KtIgWoEUghlJbbMVfjeZz73xrW79Tw5wj6Re6P++Q9EpJv5trGKAuWgRiaEmL54gO0WDDOqRFYHpask2UaBENV3KL7HmKIKwV72C4+6d7fj4p5yzAeGgRiKH5LdIhmq/5HUq0iDagRSCGclus/Veb2QYzu8HMtpnZmXVvDwzibn1PVYx6XprZiWZ2n5l9t3t6f9Xb1mFmF5rZZxaeupedvZj77vNYtIgkaLH2Y9Eikhi3xRI77N4nLWLiBnVIi0MfjxYxcbw+rf1YdIgkSm2x1p6nZjYj6XxJL5e0XdJWM9vs7j9YzOCAL+Jb22o8L7/u7q8Z87ZV/cOQy7425n0+CS0iFVqshxaRyrgtltihRItIg21ifbSIFGixHjpEKiW3WPdj+8dJ2ubuN0qSmV0s6WRJRIhFcZnmfOzdvxfzvJzoc9rdv2RmyyQ9S5JLut7dH+1e9k/j3OcAtIgkaLE2WkQSi2ixxA4lWkQCbBPHQouYOFqsjQ6RRMkt1v2r10q6tef37d3zgEWbm1/S91RB1efli8zs38zsMjN7Ts3bDmVm7zSzFWb2akk3qHMA4nMk/T8ze1Xd+6uAFpEMLdZCi0hmzBZL7FCiRSQyqENaHIgWkQSvT2uhQyRTaot19zztdyAD7zPQ6ZJOl6T161fVfAiUyH3ogYfXmNk1Pb9vdPeNPb9XeV5+R9Lh7v5gN4ovSzqy4m2reIu7n2NmH5b0Ene/RZLM7DBJl0vaMsZ9DlO7xbXLl054BLQRLdZWu8VVMysnPALaaBEtltihNEaLh+2bYAq0yogOJVrsZ+TsvR3uM7NPghHQNrw+ra32NnGZ8foUo5XcYt3F0+2S1vf8vk7SjoVX6v4PtFGSnv/8teP8QSjQkIMM73L3Y4fcdOTz0t3v7/l5i5l93MzWVLltRbtXJnftDrD7WLeY2Z1j3N8otVt87n4raBGV0GIttVs8dK8DaRGVjNliiR1KY7T4y4ctoUWMNOJLMGjxyar83T/r8KClB9EhKuH1aS21t4mrZnh9impKbbHu4ulWSUea2RGSbpN0iqT/VPM+gCdxaTHHzhj5vDSzgyX9xN3dzI5T55AVd0m6d9RtK/qOmZ0v6dtm9reSvtA9/1RJ/zrG/Y1Ci0iCFmujRSSxiBZL7FCiRSTANnEstIiJo8Xa6BBJlNxircVTd581szPU2Z11RtKF7n7dGAMDC4z8WNRAg56XZva27uUXSHqDpD8ys1lJD0s6xd1d0qSe038g6Q8lHSvpKd3fd5v455FoEenQYh20iHTGa7HEDiVaRCpsE+uiRaRBi3XQIdIpt8W6e57K3bcozfFxUDB3aW74x6JG3P7Jz8tufLt/Pk/SeVVvO8bjPyLp3MXcxxiPSYuYOFoc6zFpERO3mBZL7LD7uLSIiWKbOPbj0iImihbHekw6xMSV3GLtxVMglflFRBiFmZ017HJ3P3taswDjokUghqa3SIdog6Z3KNEi2oEWgRhKbZHFU4TgssUcOyOSB3p+3kvSqyXdIImPSaARaBGIoSUt0iEarSUdSrSIhqNFIIaSW2TxFGEsZvfvKNz9I72/m9mHJV3h7qdlGgmojRaBGJreIh2iDZreoUSLaAdaBGIotUUWTxGCuzQ33/wI+1glaX3uIYCqaBGIoaUt0iEapaUdSrSIhqFFIIaSW2TxFCG41Irdv83se5J2/9tkiaSDJH0g20BATbQIxNCGFukQTdeGDiVaRPPRIhBDyS2yeIogrBUHHpb0mp6fZyX9xN3ncg0D1EeLQAytaJEO0XCt6FCiRTQeLQIxlNti85eM0QqddzCs76lJ3P0WSQ+q8y7GnpLWmdlFZvZUM1uVdzpgNFoEYmhDi3SIphvWIS0C09OGbaJEi2i+kltkz1OE0bTg+jGzz0l6sZ747W1Pl3S0pI9L+kSOuYA6aBGIoekt0iHaoOkdSrSIdqBFIIZSW2TxFCG4tyNCSb/o7kf0nmFm33H35+caCKiDFoEYWtIiHaLRWtKhRItoOFoEYii5RRZPEUZLjp1xWZ/zrpj6FMAi0CIQQwtapEM0Xgs6lGgRLUCLQAyltsjiKUJwNe84GQNcZ2aHufstZvYSSS+Q9D9yDwVURYtADC1pkQ7RaC3pUKJFNBwtAjGU3CJfGIUQ2nLgYUnvkXSbmR0s6dOS9pL0hbwjAdXRIhBDS1qkQzRaW74wSrSIhmvJNlGiRTRcyS2yeIow5rz/qWEed/c5Sa+W9Lfu/kFJ+2SeCaiFFoEYWtAiHaLxBnVIi8B0taBDiRbRAqW2yMf2EYK7NNu8dyv6ecDM/ljSaZJ+x8xMdIYGoUUghpa0SIdotJZ0KNEiGo4WgRhKbpE9TxGGu/U9NcybJR0u6UPu/m+Slkv6o7wjAfXQIhBDC1qkQzTeoA5pEZiuFnQo0SJaoNQWeZcDIXSOnZF7isVz95slvavn94ckfTPfREA9tAjE0IYW6RBN14YOJVpE89EiEEPJLbJ4ihBcrdn9G2g0WgRioEUgPzoEYqBFIIaSW0y+eLpkyV5avuLpqR9mIu54eEfuEWo591Nvyj1CDR8fcXkjv6GtUfZYv0yr/vo5uceo5A/m78s9Qi0f+tXTco9Qw/kjLqfF1B6ZM/3w3mYcNWf2p8tyj1DLc1Y3498dy3bOVbgWLab2o9vW6Df/yxtzj1HJcfvP5x6hUHSY2rqD79SH/vSC3GNUMnfv8twj1DLzK4fmHqGG+0dcToupPf2YPfXFr6/NPUYlu96yOfcItdx11ytyj1DZC/7lshHXKLdF9jxFDN45+DCAzGgRiIEWgfzoEIiBFoEYCm6RxVOE0Nn9O/cUAGgRiIEWgfzoEIiBFoEYSm6RxVOE0DnwcJm7fwOR0CIQAy0C+dEhEAMtAjGU3CKLpwijDd/aBrQBLQIx0CKQHx0CMdAiEEOpLbJ4ihA672DkngIALQIx0CKQHx0CMdAiEEPJLbJ4ihhcmi80QiAUWgRioEUgPzoEYqBFIIaCW2TxFCGU/A4GEAktAjHQIpAfHQIx0CIQQ8ktsniKEEqOEIiEFoEYaBHIjw6BGGgRiKHkFlk8RRilRghEQ4tADLQI5EeHQAy0CMRQaotLcg8ASJ13MOa9/6kKM9tgZjeY2TYzO7PP5b9tZt/rnq4ys2N6LrvJzP7dzL5rZtdM7I8CGogWgRgW0yIdApMxrENaBKaH16dADCW3yJ6niMHHfwfDzGYknS/p5ZK2S9pqZpvd/Qc9V/uxpJe6+z1mdpKkjZKO77n8Ze6+a7wJgBahRSCGMVukQ2CC2CYCMdAiEEPBLbJ4ihAWeeyM4yRtc/cbJcnMLpZ0sqSfRejuV/Vc/2pJ68Z+NKDFaBGIYREt0iEwIWwTgRhoEYih5Bb52D7CmJvvf6pgraRbe37f3j1vkNMkXdbzu0v6qplda2an150baBtaBGIYs0U6BCZoUIe0CEwXr0+BGEptkT1PEYJLmvWBb2GsWXBMi43uvrHndxtwl09iZi9TJ8Jf6Tn7BHffYWYHSrrCzK539yurTw+0By0CMSyiRToEJmREhxItAlPB61MghpJbZPEUYQx5abrL3Y8dctPtktb3/L5O0o6FVzKz50r6lKST3P2unz2u+47uP3ea2aXq7E7OBhHFokUghjFbpENggkZ8OpEWgSnh9SkQQ6kt8rF9hODumhtwqmCrpCPN7AgzWyrpFEmbe69gZodJ+pKk33H3/9tz/goz22f3z5JeIen7E/qzgMahRSCGRbRIh8CEDOuQFoHp4fUpEEPJLdbe89TMLpT0Gkk73f3oyY+EEnV2/x7ztu6zZnaGpMslzUi60N2vM7O3dS+/QNL7Je0v6eNmJkmz3XdFDpJ0afe8PSR93t2/sri/ZjpoESnQYn20iBTGbZEO6RCTwzaxPlpECrRYHy0ihZJbHOdj+5sknSfps5MdBSXrfGvb+F/b5u5bJG1ZcN4FPT+/VdJb+9zuRknHjP3AeW0SLWLCaHEsm0SLmLDFtEiHwGSwTRzLJtEiJowWx7JJtIgJK7nF2h/b7x6Q9e4Es6Bw7t73hP5oEanQYj20iFRosTo6RCqDOqTF/mgRqdBhPbSIVEptkS+MQggVvs0UwBTQIhADLQL50SEQAy0CMZTcYpLFUzM7XdLpknTYYfuneAi00Nyo7zNFbb0trl+7IvM0aApanLzeFpd3jnUOjESLk9fb4t62MvM0aAI6nLwn/LfivpmHQWPQ4uQ94b8V16/KPA2aotQWa39svwp33+jux7r7sQcc8JQUD4GWcXfN+nzfE8bX2+Ka/ffKPQ4agBbT6G1xryV75x4HDUCLafS2uNRoEcMN65AWx/eE16crLfc4aAC2iWk8ocU17GiD0UpukY/tIwSXNKf2BwdER4tADLQI5EeHQAy0CMRQcou19zw1s4skfUvSM81su5mdNvmxUKJ5ed8T+qNFpEKL9dAiUqHF6ugQqQzqkBb7o0WkQof10CJSKbXF2nueuvupKQZB2Vxe7DsY46JFpECL9dEiUqDFeugQKdBhfbSIFGixPlpECiW3yMf2EYNJszaXewoAtAjEQItAfnQIxECLQAwFt8jiKULovINRZoRAJLQIxECLQH50CMRAi0AMJbfI4inCmLcyd/8GoqFFIAZaBPKjQyAGWgRiKLVFFk8Rgss1q9ncYwDFo0UgBloE8qNDIAZaBGIouUUWTxGCyzVnZUYIREKLQAy0CORHh0AMtAjEUHKLLJ4iCNecHs89BABaBIKgRSA/OgRioEUghnJbZPEUIZS8+zcQCS0CMdAikB8dAjHQIhBDyS2yeIogOt/bBiA3WgRioEUgPzoEYqBFIIZyW2TxFCG4pLlC38EAIqFFIAZaBPKjQyAGWgRiKLlFFk8Rghd87AwgEloEYqBFID86BGKgRSCGkltk8RRBuOa8zAiBWGgRiIEWgfzoEIiBFoEYym2RxVMEUe6xM4BYaBGIgRaB/OgQiIEWgRjKbZHFU4Tgcs15mcfOACKhRSAGWgTyo0MgBloEYii5RRZPEYJ7ubt/A5HQIhADLQL50SEQAy0CMZTcIounCMI1X2iEQCy0CMRAi0B+dAjEQItADOW2yOIpgnDNF7r7NxALLQIx0CKQHx0CMdAiEEO5LbJ4ihBcknuZBx4GIqFFIAZaBPKjQyAGWgRiKLlFc/e0D2B2p6SbE9z1Gkm7EtxvCswqHe7uBwy6cMmSpb50jwP7Xvbo47dd6+7HJpipKLQoiVklWswuUYtNem5LzZo3xaxDO5RocRposVGzShlaHNahRIuTwOtTScwq8fo0O1qUxKwSLQ6UfM/TUf9xMC4zu6Yp/8cwawVe7u7f00KLzFoJLSaXosUmPbelZs1Li+1VeotNmlXKNC8dJsfrU2athBaTo0VmraTgFvnYPkJwubzQAw8DkdAiEAMtAvnRIRADLQIxlNziktwDALu5z/c9VWFmG8zsBjPbZmZn9rnczOyj3cu/Z2bPr3pboDS0CMQwbot0CEzOoA5pEZguXp8CMZTaYpMXTzfmHqAGZh3J5ZrtexrFzGYknS/pJElHSTrVzI5acLWTJB3ZPZ0u6RM1bovheH6nQYu0WEeTnttSs+ZtVIt0mB3P7XQyzDu4Q1oMr0nPb2YdidenDcbzOw1anHKLjV08dffGPLGZtdIjy32276mC4yRtc/cb3f0xSRdLOnnBdU6W9FnvuFrSajM7pOJtMQTP7zRokRbraNJzW2rWvA1skQ4z4rmdTp55B3dIi7E16fnNrJUemdenDcXzOw1anH6LjV08RQv5XP/TaGsl3drz+/bueVWuU+W2QFloEYhhvBbpEJikQR3SIjBdvD4FYii0xcYtnprZs8zsW2b2qJm9O/c8w+Q+JkMdZnahme00s+/nmcAvdz1+bb+TpGVmdk3P6fQFN7Z+d1jxOlVuiz5oMQ1aHHpb9EGLaTS4RTrMhBbTyNvi4A5pMaYmdSg1p8UGbxMlWsyiSS02pUOJFkfcNqk9pvlgE3K3pHdIen3mOYbqOSbDy9VZFd9qZpvd/Qd5Jxtok6TzJH02x4O7+4ZF3Hy7pPU9v6+TtKPidZZWuC36o8U0NokWB90W/dFiGpvUzBbpMB9aTGOTMrXINrGRGtGh1LgWN6mZ20SJFnNpRIsN61CixWG3Tapxe566+0533yrp8dyzjJD9mAx1uPuV6vwLrom2SjrSzI4ws6WSTpG0ecF1Nkt6s3W8UNJ97n57xduiD1pMgxZpsS5aTKPBLdJhJrSYBi3SYh0N6lBqUIsN7lCixSwa1GJjOpRoMWeLTdzztCn6HZPh+EyztJq7z5rZGZIulzQj6UJ3v87M3ta9/AJJWyS9StI2ST+V9HvDbpvhz0A6tDgltIgRaHEK6BAV0OIU0CIqoMUpoEWMQIdT0vQWWTxNJ/sxGUri7lvUCa33vAt6fnZJb696W7QKLU4RLWIIWpwSOsQItDgltIgRaHFKaBFD0OEUNbnFRnxs38zebmbf7Z4OzT1PRVWO5wA0Ci0CMdAiEAMtAvk1tEOJFtEyDW2RDlFJIxZP3f18d39e99SUJ3L2YzIAk0aLQAy0CMRAi0B+De1QokW0TENbpENUYp29YpvDzA6WdI2kp0ial/SgpKPc/f6sg/VhZq+SdI5+fkyGv8w70WBmdpGkEyWtkfQTSWe5+6ezDoXQaDENWkRdtJgGLaIuWkyDFlFHkzqUmtMiHaKuJrXYlA4lWsypcYunAAAAAAAAADANjfjYPgAAAAAAAABMG4unAAAAAAAAANAHi6cAAAAAAAAA0AeLpwAAAAAAAADQB4unAAAAAAAAANAHi6cAAAAAAAAA0AeLpwAAAAAAAADQB4unAAAAAAAAANDH/wdK1ojsfFGX2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convolution Layer Name: conv2d_16, Shape: 3 x 3, Number of Channels: 12.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 24 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convolution Layer Name: conv2d_17, Shape: 3 x 3, Number of Channels: 24.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 48 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_filters(model)                                # visualize all of the CNN filter weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eaXVG_WK1Fof"
   },
   "source": [
    "These are small 3 x 3 filters, but we can recognize some common structures:\n",
    "\n",
    "* averaging / smoothing\n",
    "\n",
    "* gradient\n",
    "\n",
    "* edge / sharpen (sobel)\n",
    "\n",
    "and combinations of these structures.\n",
    "\n",
    "* it makes sense that averaging, edge and gradient filters would differentiate the random and smooth images.\n",
    "\n",
    "#### Visualize the Convolution Feature Maps\n",
    "\n",
    "Let's visualize the feature maps after each convolution for the classification prediction problem \n",
    "\n",
    "* you can specify the image below from 0 - 99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 383
    },
    "executionInfo": {
     "elapsed": 814,
     "status": "ok",
     "timestamp": 1621130838486,
     "user": {
      "displayName": "HONGGEUN JO",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjjMrjKxVvTiBoV4qCmbZQiE5mDJaodEzKxwowf=s64",
      "userId": "13788572619033134064"
     },
     "user_tz": 300
    },
    "id": "XnLPYab81Fof",
    "outputId": "88ec9c8a-5b15-4a2c-b7f0-2310af83ed4b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convolution Layer Name: conv2d_15, Shape: 12 x 12, Number of Channels: 6.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convolution Layer Name: conv2d_16, Shape: 6 x 6, Number of Channels: 12.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convolution Layer Name: conv2d_17, Shape: 3 x 3, Number of Channels: 24.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 49 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = 55                                              # selected prediction case image from 0 - 99\n",
    "visualize_convolution_feature_maps(model, image)        # function to visualize the convolution feature maps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These feature maps in each layer demonstrate the extraction of information from the original images:\n",
    "\n",
    "* note the identification of the artifact over each convolution filter.\n",
    "\n",
    "#### Visualize the Max Pooling Feature Maps\n",
    "\n",
    "Let's visualize the feature maps after max pooling operations for our classification prediction problem.\n",
    "\n",
    "* you can specify the image below from 0 - 99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Pooling Layer Name: max_pooling2d_15, Shape: 6 x 6, Number of Channels: 6.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Pooling Layer Name: max_pooling2d_16, Shape: 3 x 3, Number of Channels: 12.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Pooling Layer Name: max_pooling2d_17, Shape: 1 x 1, Number of Channels: 24.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = 55                                              # selected prediction case image from 0 - 99\n",
    "visualize_max_pooling_feature_maps(model, image)        # function to visualize the max pooling feature maps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "58f4xmgS6ui8"
   },
   "source": [
    "\n",
    "#### Visualize the Output Layer\n",
    "\n",
    "Let's visualize the output layer which contains the probability to be each categories (e.g., random or smooth)\n",
    "\n",
    "* you can specify the image below from 0 - 99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 222
    },
    "executionInfo": {
     "elapsed": 456,
     "status": "ok",
     "timestamp": 1621130868139,
     "user": {
      "displayName": "HONGGEUN JO",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjjMrjKxVvTiBoV4qCmbZQiE5mDJaodEzKxwowf=s64",
      "userId": "13788572619033134064"
     },
     "user_tz": 300
    },
    "id": "H808m7u-6nK-",
    "outputId": "b6310f05-53af-4c99-a8bc-63d7eba1d79d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = 55                                              # selected prediction case image from 0 - 99\n",
    "visualize_last_layers(model, image)                     # function to visualize the output layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a1Mas1bP1Fof"
   },
   "source": [
    "#### Other Convolutional Neural Network Architecture Examples\n",
    "\n",
    "Some other examples of layers that could be added to the above CNN\n",
    "\n",
    "1. More dense layers, ANN feed forward\n",
    "\n",
    "```python\n",
    "    model.add(layers.Dense(512))\n",
    "    model.add(layers.BatchNormalization(momentum=0.8))\n",
    "    model.add(layers.ReLU())\n",
    "    model.add(layers.Dropout(0.25))\n",
    "```\n",
    "\n",
    "2. Max pooling 2D layers\n",
    "\n",
    "```python\n",
    "    model.add(layers.MaxPooling2D((2, 2)))\n",
    "```\n",
    "\n",
    "#### Visualize the Testing Images and Predicted Classification Probabilities\n",
    "\n",
    "Let's look at all the testing images and the predicted classification probabilities from our our trained CNN.\n",
    "\n",
    "* we report the true label, and then the greatest category probability, > 50% (argmax), and the associated category from our CNN \n",
    "\n",
    "* for example, 'connected, 64% connected' indicates a true label of connected (first connected) and the connected (second connected) category got the largest predicted probability and that probability was 64%. Given an argmax assignment, this is a correct prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_testing_predictions(model)                    # visualize testing images and probability for correct category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summarize the Performance of our Image Classification CNN\n",
    "\n",
    "Let's summarize the performance of our CNN over all training and testing images \n",
    "\n",
    "* we use boxplots to report the training and testing images prediction probabilities \n",
    "\n",
    "* we show the predicted probability of connected or not connected geobody images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_boxplot(model)                                  # visualize the prediction probability box plots by correct category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To better understand our model, we are going to test it's abilities by running two test cases on it\n",
    "#### Test Case 1: Inverted Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = ['connected', 'not connected']\n",
    "for i in range(0,len(X_test)):\n",
    "    array = X_test[i,:,:,0]\n",
    "    X_test[i,:,:,0] = np.where(array < .15, 1, 0)  \n",
    "\n",
    "for i in range(0,len(X_test)):\n",
    "    array = X_test[i,:,:,0]\n",
    "    new_array = np.where(array < .15, 0, 1)\n",
    "    new_array = new_array.tolist()\n",
    "    if maxAreaOfIsland(new_array) > 72:\n",
    "        y_test[i] = 0\n",
    "    else:\n",
    "        y_test[i] = 1\n",
    "\n",
    "visualize_testing_predictions(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test Case 2: Images that lie outside of our original data's sand proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "XX = np.zeros([4,nx,ny,1])\n",
    "yy_test = np.zeros(ntest,dtype = int)\n",
    "c, r = 4, 1                                             # visualize labelled synthetic images\n",
    "XX[0,0:12,4:6,0] = 1\n",
    "XX[1,4:6,0:12,0] = 1\n",
    "XX[2,2:10,0:12,0] = 1\n",
    "XX[3,0:12,2:10,0] = 1\n",
    "for i in range(0,len(XX)):\n",
    "    array = XX[i,:,:,0]\n",
    "    new_array = np.where(array < .15, 0, 1)\n",
    "    new_array = new_array.tolist()\n",
    "    if maxAreaOfIsland(new_array) > 72:\n",
    "        yy_test[i] = 0\n",
    "    else:\n",
    "        yy_test[i] = 1\n",
    "for i in range(c*r):\n",
    "    plt.subplot(r,c,i+1)\n",
    "    fig = plt.imshow(XX[i,:,:,0],cmap=cmap)\n",
    "    fig.axes.get_xaxis().set_visible(False); fig.axes.get_yaxis().set_visible(False)\n",
    "    plt.title(names[yy_test[i]])\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.3, top=0.5, wspace=.5, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualize_testing_predictions2(model):               # visualize testing images and predicted probabilities \n",
    "    y_test_predict = model.predict(XX[:,:,:,0].reshape([4,nx,ny,-1])) # predict over the testing images \n",
    "    y_test_predict_cat = np.argmax(y_test_predict,axis = 1) # assign the maximum probability category\n",
    "    \n",
    "    c, r = 4, 1                                         \n",
    "    plt.figure(figsize = (15,15))\n",
    "    for i in range(c*r):\n",
    "        plt.subplot(r,c,i+1)\n",
    "        fig = plt.imshow(XX[i,:,:,0],cmap=cmap)\n",
    "        fig.axes.get_xaxis().set_visible(False); fig.axes.get_yaxis().set_visible(False)\n",
    "        icat = y_test_predict_cat[i]\n",
    "        plt.title('Image ' + str(i) + ': ' + names[yy_test[i]] + ', ' + str(np.round(y_test_predict[i,icat]*100,2)) + \"% \" + names[icat] )\n",
    "\n",
    "            \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=1.3, top=1.6, wspace=0.01, hspace=0.3)\n",
    "    plt.show()\n",
    "visualize_testing_predictions2(model)"
   ]
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    "### Conclusion:\n",
    "\n",
    "Although the model is able to distinguish porous media from non-porous in the original images, it struggles with edge detection around the sides of the synthetic images. Specifically, in Test Case 1, the model failed to correctly identify all of the  connected images as all the porous (yellow) cells lied on the exterior of the images. This highlights the importance of using padding when creating a model. The model performed well with images *exceeding* the train and test data sand proportions. This proves that the proportions of porous media have little to no effect on the model's effectivness. Convolutional neural networks such as this one are applicable to the oil and gas industry as they can save time and money when searching for profitable reservoirs."
   ]
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   "source": [
    "#### Comments\n",
    "\n",
    "This was a very simple convolutional neural network workflow to support experiential learning with fast experientation.  \n",
    "\n",
    "The Texas Center for Data Analytics and Geostatistics has many other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available [here](https://github.com/GeostatsGuy/PythonNumericalDemos), along with a package for geostatistics in Python called [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy). \n",
    "  \n",
    "We hope this was helpful,\n",
    "\n",
    "*John Eric, Honggeun,* and *Michael*\n",
    "\n",
    "***\n",
    "\n",
    "#### More on Michael Pyrcz and the Texas Center for Data Analytics and Geostatistics:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
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